From b0405abadaa4acdeb2c3ee713b5cc12680994a3f Mon Sep 17 00:00:00 2001 From: DaDeng Date: Sun, 26 Nov 2023 14:19:41 +0800 Subject: [PATCH] add poetry project.toml --- .DS_Store | Bin 6148 -> 6148 bytes build/lib/cntext/__init__.py | 12 - build/lib/cntext/dictionary.py | 543 ----------- build/lib/cntext/files/ADV_CONJ.pkl | Bin 2343 -> 0 bytes build/lib/cntext/files/AFINN.pkl | Bin 26787 -> 0 bytes build/lib/cntext/files/ANEW.pkl | Bin 66647 -> 0 bytes build/lib/cntext/files/ChineseEmoBank.pkl | Bin 144219 -> 0 bytes ...hineseFinancialFormalUnformalSentiment.pkl | Bin 66447 -> 0 bytes .../cntext/files/Chinese_Digitalization.pkl | Bin 1653 -> 0 bytes build/lib/cntext/files/Chinese_FLS.pkl | Bin 2143 -> 0 bytes ..._Loughran_McDonald_Financial_Sentiment.pkl | Bin 55342 -> 0 bytes build/lib/cntext/files/Concreteness.pkl | Bin 1115097 -> 0 bytes build/lib/cntext/files/DUTIR.pkl | Bin 350601 -> 0 bytes build/lib/cntext/files/HOWNET.pkl | Bin 202713 -> 0 bytes build/lib/cntext/files/HuLiu.pkl | Bin 77508 -> 0 bytes build/lib/cntext/files/LSD2015.pkl | Bin 116927 -> 0 bytes .../Loughran_McDonald_Financial_Sentiment.pkl | Bin 50433 -> 0 bytes build/lib/cntext/files/NRC.pkl | Bin 148612 -> 0 bytes build/lib/cntext/files/STOPWORDS.pkl | Bin 9407 -> 0 bytes build/lib/cntext/files/geninqposneg.pkl | Bin 38829 -> 0 bytes build/lib/cntext/files/sentiws.pkl | Bin 461524 -> 0 bytes build/lib/cntext/io.py | 117 --- build/lib/cntext/mind.py | 207 ---- build/lib/cntext/similarity.py | 114 --- build/lib/cntext/stats.py | 294 ------ cntext.egg-info/PKG-INFO | 880 ------------------ cntext.egg-info/SOURCES.txt | 32 - cntext.egg-info/dependency_links.txt | 1 - cntext.egg-info/requires.txt | 12 - cntext.egg-info/top_level.txt | 1 - cntext/.DS_Store | Bin 6148 -> 6148 bytes dist/.DS_Store | Bin 0 -> 6148 bytes dist/cntext-1.8.8-py3-none-any.whl | Bin 963943 -> 964307 bytes dist/cntext-1.8.8.tar.gz | Bin 0 -> 964894 bytes .../01-stats-checkpoint.ipynb | 219 +++-- examples/00-cntext.ipynb | 30 +- examples/01-stats.ipynb | 59 +- pyproject.toml | 37 + 38 files changed, 242 insertions(+), 2316 deletions(-) delete mode 100644 build/lib/cntext/__init__.py delete mode 100644 build/lib/cntext/dictionary.py delete mode 100644 build/lib/cntext/files/ADV_CONJ.pkl delete mode 100644 build/lib/cntext/files/AFINN.pkl delete mode 100644 build/lib/cntext/files/ANEW.pkl delete mode 100644 build/lib/cntext/files/ChineseEmoBank.pkl delete mode 100644 build/lib/cntext/files/ChineseFinancialFormalUnformalSentiment.pkl delete mode 100644 build/lib/cntext/files/Chinese_Digitalization.pkl delete mode 100644 build/lib/cntext/files/Chinese_FLS.pkl delete mode 100644 build/lib/cntext/files/Chinese_Loughran_McDonald_Financial_Sentiment.pkl delete mode 100644 build/lib/cntext/files/Concreteness.pkl delete mode 100644 build/lib/cntext/files/DUTIR.pkl delete mode 100644 build/lib/cntext/files/HOWNET.pkl delete mode 100644 build/lib/cntext/files/HuLiu.pkl delete mode 100644 build/lib/cntext/files/LSD2015.pkl delete mode 100644 build/lib/cntext/files/Loughran_McDonald_Financial_Sentiment.pkl delete mode 100644 build/lib/cntext/files/NRC.pkl delete mode 100644 build/lib/cntext/files/STOPWORDS.pkl delete mode 100644 build/lib/cntext/files/geninqposneg.pkl delete mode 100644 build/lib/cntext/files/sentiws.pkl delete mode 100644 build/lib/cntext/io.py delete mode 100644 build/lib/cntext/mind.py delete mode 100644 build/lib/cntext/similarity.py delete mode 100644 build/lib/cntext/stats.py delete mode 100644 cntext.egg-info/PKG-INFO delete mode 100644 cntext.egg-info/SOURCES.txt delete mode 100644 cntext.egg-info/dependency_links.txt delete mode 100644 cntext.egg-info/requires.txt delete mode 100644 cntext.egg-info/top_level.txt create mode 100644 dist/.DS_Store create mode 100644 dist/cntext-1.8.8.tar.gz create mode 100644 pyproject.toml diff --git a/.DS_Store b/.DS_Store index 24ae80321cbe9df5369f33f8d8900c5d7e91c608..20724dce09fa6fcce3171c6054583e31e0075551 100644 GIT binary patch delta 79 zcmZoMXffEJ&ce8T@*LJeF7fJWBTF3x6El;^|JbA%J0~Atk!S3h{D5UQUIm*&*fuk6 JX65+L4*(9=8W8{h delta 42 xcmZoMXffEJ&N8{4ZO`OKEIWX7DTkS*rH+D;smbI&Z1$T&*)}k4X6N|J4**Ic4$c4o diff --git a/build/lib/cntext/__init__.py b/build/lib/cntext/__init__.py deleted file mode 100644 index 41a19dc..0000000 --- a/build/lib/cntext/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -__version__ = "1.8.8" - -from cntext.dictionary import SoPmi, W2VModels, co_occurrence_matrix, Glove -from cntext.similarity import jaccard_sim, minedit_sim, simple_sim, cosine_sim -from cntext.stats import load_pkl_dict, dict_pkl_list, term_freq, readability, sentiment, sentiment_by_valence, sentiment_by_weight -from cntext.mind import Text2Mind -from cntext.io import get_files, read_file, read_files, detect_encoding - - - - - diff --git a/build/lib/cntext/dictionary.py b/build/lib/cntext/dictionary.py deleted file mode 100644 index dd23969..0000000 --- a/build/lib/cntext/dictionary.py +++ /dev/null @@ -1,543 +0,0 @@ -import jieba.posseg as pseg -import math,time -from gensim.models import word2vec -from pathlib import Path -from nltk.tokenize import word_tokenize -import multiprocessing -from collections import defaultdict -import numpy as np -import pandas as pd -from cntext.stats import load_pkl_dict -from gensim.models.phrases import Phrases, ENGLISH_CONNECTOR_WORDS -from collections import Counter -from mittens import GloVe -from sklearn.feature_extraction.text import CountVectorizer -import jieba - - - -STOPWORDS_zh = load_pkl_dict(file='STOPWORDS.pkl')['STOPWORDS']['chinese'] -STOPWORDS_en = load_pkl_dict(file='STOPWORDS.pkl')['STOPWORDS']['english'] - - -class SoPmi: - """ - Only support chinese now. - """ - def __init__(self, cwd, input_txt_file, seedword_txt_file, lang='chinese'): - """ - :param cwd: The current folder path of the code, just set cwd=os.getcwd() will be okay! - :param input_txt_file: The path of the corpus file - :param seedword_txt_file: the path of manually selected seed word file - :param lang: set the language for SoPmi - """ - self.cwd = cwd - self.text_file = input_txt_file - self.seedword_txt_file = seedword_txt_file - self.lang=lang - - - '''分词''' - def seg_corpus(self, train_data, seedword_txt_file): - #Add words to the jieba dictionary to ensure that the segmentation can't cut the seed emotional words - if self.lang=='chinese': - sentiment_words = [line.strip().split('\t')[0] for line in open(seedword_txt_file, encoding='utf-8')] - for word in sentiment_words: - jieba.add_word(word) - - seg_data = list() - count = 0 - for line in open(train_data, encoding='utf-8'): - line = line.strip() - count += 1 - if line: - seg_data.append([word.word for word in pseg.cut(line) if word.flag[0] not in ['u','w','x','p','q','m']]) - else: - continue - elif self.lang=='english': - pass - return seg_data - - - - '''统计搭配次数''' - def collect_cowords(self, seedword_txt_file, seg_data): - def check_words(sent): - if set(sentiment_words).intersection(set(sent)): - return True - else: - return False - - cowords_list = list() - window_size = 5 - count = 0 - sentiment_words = [line.strip().split('\t')[0] for line in open(seedword_txt_file, encoding='utf-8')] - for sent in seg_data: - count += 1 - if check_words(sent): - for index, word in enumerate(sent): - if index < window_size: - left = sent[:index] - else: - left = sent[index - window_size: index] - if index + window_size > len(sent): - right = sent[index + 1:] - else: - right = sent[index: index + window_size + 1] - context = left + right + [word] - if check_words(context): - for index_pre in range(0, len(context)): - if check_words([context[index_pre]]): - for index_post in range(index_pre + 1, len(context)): - cowords_list.append(context[index_pre] + '@' + context[index_post]) - return cowords_list - - - def collect_candiwords(self, seg_data, cowords_list, seedword_txt_file): - """ - Calculate the So-Pmi value - :param seg_data: - :param cowords_list: - :param seedword_txt_file: - :return: - """ - def compute_mi(p1, p2, p12): - '''Mutual information calculation function''' - return math.log2(p12) - math.log2(p1) - math.log2(p2) - '''统计词频''' - def collect_worddict(seg_data): - word_dict = dict() - for line in seg_data: - for word in line: - if word not in word_dict: - word_dict[word] = 1 - else: - word_dict[word] += 1 - all = sum(word_dict.values()) - return word_dict, all - '''统计词共现次数''' - def collect_cowordsdict(cowords_list): - co_dict = dict() - candi_words = list() - for co_words in cowords_list: - candi_words.extend(co_words.split('@')) - if co_words not in co_dict: - co_dict[co_words] = 1 - else: - co_dict[co_words] += 1 - return co_dict, candi_words - '''收集种子情感词''' - def collect_sentiwords(seedword_txt_file, word_dict): - pos_words = set([line.strip().split('\t')[0] for line in open(seedword_txt_file, encoding='utf-8') if - line.strip().split('\t')[1] == 'pos']).intersection(set(word_dict.keys())) - neg_words = set([line.strip().split('\t')[0] for line in open(seedword_txt_file, encoding='utf-8') if - line.strip().split('\t')[1] == 'neg']).intersection(set(word_dict.keys())) - return pos_words, neg_words - '''计算sopmi值''' - def compute_sopmi(candi_words, pos_words, neg_words, word_dict, co_dict, all): - pmi_dict = dict() - for candi_word in set(candi_words): - pos_sum = 0.0 - neg_sum = 0.0 - for pos_word in pos_words: - p1 = word_dict[pos_word] / all - p2 = word_dict[candi_word] / all - pair = pos_word + '@' + candi_word - if pair not in co_dict: - continue - p12 = co_dict[pair] / all - pos_sum += compute_mi(p1, p2, p12) - - for neg_word in neg_words: - p1 = word_dict[neg_word] / all - p2 = word_dict[candi_word] / all - pair = neg_word + '@' + candi_word - if pair not in co_dict: - continue - p12 = co_dict[pair] / all - neg_sum += compute_mi(p1, p2, p12) - - so_pmi = pos_sum - neg_sum - pmi_dict[candi_word] = so_pmi - return pmi_dict - - word_dict, all = collect_worddict(seg_data) - co_dict, candi_words = collect_cowordsdict(cowords_list) - pos_words, neg_words = collect_sentiwords(seedword_txt_file, word_dict) - pmi_dict = compute_sopmi(candi_words, pos_words, neg_words, word_dict, co_dict, all) - return pmi_dict - - - def save_candiwords(self, pmi_dict): - """ - save the so-pmi result - :param pmi_dict: - :return: - """ - def get_tag(word): - if word: - return [item.flag for item in pseg.cut(word)][0] - else: - return 'x' - pos_dict = dict() - neg_dict = dict() - Path(self.cwd).joinpath('output').mkdir(exist_ok=True) - Path(self.cwd).joinpath('output', 'sopmi_candi_words').mkdir(exist_ok=True) - negfile= Path(self.cwd).joinpath('output', 'sopmi_candi_words', 'neg.txt') - posfile= Path(self.cwd).joinpath('output', 'sopmi_candi_words', 'pos.txt') - - f_neg = open(negfile, 'w+', encoding='utf-8') - f_pos = open(posfile, 'w+', encoding='utf-8') - - for word, word_score in pmi_dict.items(): - if word_score > 0: - pos_dict[word] = word_score - else: - neg_dict[word] = abs(word_score) - - for word, pmi in sorted(pos_dict.items(), key=lambda asd:asd[1], reverse=True): - f_pos.write(word + ',' + str(pmi) + ',' + 'pos' + ',' + str(len(word)) + ',' + get_tag(word) + '\n') - for word, pmi in sorted(neg_dict.items(), key=lambda asd:asd[1], reverse=True): - f_neg.write(word + ',' + str(pmi) + ',' + 'neg' + ',' + str(len(word)) + ',' + get_tag(word) + '\n') - f_neg.close() - f_pos.close() - return - - - - def sopmi(self): - """ - main function(method) of SoPmi - :return: - """ - print('Step 1/4:...Preprocess Corpus ...') - start_time = time.time() - seg_data = self.seg_corpus(self.text_file, self.seedword_txt_file) - print('Step 2/4:...Collect co-occurrency information ...') - cowords_list = self.collect_cowords(self.seedword_txt_file, seg_data) - print('Step 3/4:...Calculate mutual information ...') - pmi_dict = self.collect_candiwords(seg_data, cowords_list, self.seedword_txt_file) - print('Step 4/4:...Save candidate words ...') - self.save_candiwords(pmi_dict) - end_time = time.time() - duration = end_time-start_time - duration = round(duration, 2) - print('Finish! used {0} s'.format(duration)) - - - - - - -class W2VModels(object): - def __init__(self, cwd, lang='chinese'): - """ - initialize the W2VModels - :param cwd: current work directory - :param lang: set the language for W2VModels - """ - self.cwd = cwd - self.lang = lang - self.start = time.time() - - - def __preproces(self, documents): - """ - 对数据进行预处理,分词、去除停用词; 可以加单词同类型合并的 - :param documents: 文档列表 - :return: 清洗后的文档列表 - """ - docs = [] - if self.lang=='english': - for document in documents: - document = document.lower() - document = [w for w in word_tokenize(document) if w not in STOPWORDS_en] - docs.append(document) - return docs - elif self.lang=='chinese': - for document in documents: - words = jieba.cut(document) - document = [w for w in words if w not in STOPWORDS_zh] - docs.append(document) - return docs - else: - assert 'Do not support {} language'.format(self.lang) - - - - - def train(self, input_txt_file, model_name='w2v.model', vector_size=100, window_size=6, min_count=5, ngram=False): - """ - train word2vec model for corpus - :param input_txt_file: corpus file path - :param model_name: used as model name(save) - :param vector_size: dimensionality of the word vectors. - :param window_size: window size for word2vec - :param min_count: Set the word to appear at least min_count times in the model - :param ngram: whether to take the ngram case into account,default False - :return: - """ - - documents = list(open(input_txt_file, encoding='utf-8').readlines()) - print('Step 1/4:...Preprocess corpus ...') - sents = self.__preproces(documents=documents) - duration = int(time.time()-self.start) - - sentences = [] - if self.lang=='english' and ngram==True: - phrase_model = Phrases(sents, min_count=10, threshold=1, connector_words=ENGLISH_CONNECTOR_WORDS) - for sent in sents: - sentence = phrase_model[sent] - sentences.append(sentence) - elif self.lang=='chinese' and ngram==True: - phrase_model = Phrases(sents, min_count=10, threshold=1) - for sent in sents: - sentence = phrase_model[sent] - sentence = [w.replace('_', '') for w in sentence] - sentences.append(sentence) - else: - sentences = sents - - print('Step 2/4:...Train word2vec model\n used {} s'.format(duration)) - self.model = word2vec.Word2Vec(sentences, vector_size=vector_size, window=window_size, min_count=min_count, workers=multiprocessing.cpu_count()) - modeldir = Path(self.cwd).joinpath('output', 'w2v_candi_words') - Path(self.cwd).joinpath('output').mkdir(exist_ok=True) - Path(self.cwd).joinpath('output', 'w2v_candi_words').mkdir(exist_ok=True) - modelpath = str(Path(modeldir).joinpath(model_name)) - self.model.wv.save(modelpath) - - - - def __search(self, seedword_txt_file, n=50): - seedwords = [w for w in open(seedword_txt_file, encoding='utf-8').read().split('\n') if w!=''] - self.similars_candidate_idxs = [] #the candidate words of seedwords - dictionary = self.model.wv.key_to_index - self.seedidxs = [] #transform word to index - for seed in seedwords: - if seed in dictionary: - seedidx = dictionary[seed] - self.seedidxs.append(seedidx) - for seedidx in self.seedidxs: - # sims_words such as [('by', 0.99984), ('or', 0.99982), ('an', 0.99981), ('up', 0.99980)] - sims_words = self.model.wv.similar_by_word(seedidx, topn=n) - #Convert words to index and store them - self.similars_candidate_idxs.extend([dictionary[sim[0]] for sim in sims_words]) - self.similars_candidate_idxs = set(self.similars_candidate_idxs) - - - def save(self, model_name='w2v_model'): - """ - Save word embedding data into the txt file - """ - txtdir = Path(self.cwd).joinpath('output', 'Word2Vec') - Path(self.cwd).joinpath('output', 'Word2Vec').mkdir(exist_ok=True) - w2v_file = Path(txtdir).joinpath('{}.txt'.format(model_name)) - self.model.wv.save_word2vec_format(w2v_file) - - - - def find(self, seedword_txt_file, topn=50): - """ - According to the seed word file, select the top n words with the most similar semantics - 根据种子词txt文件, 选出语义最相近的topn个词 - :param seedword_txt_file: seed word file path, only support .txt file now! - :param topn: The number of candidate words to output, the default topn=50 - :return: - """ - - seedwordsname = seedword_txt_file.split('/')[-1].replace('.txt', '') - seedwords = [w for w in open(seedword_txt_file, encoding='utf-8').read().split('\n') if w] - simidx_scores = [] - - print('Step 3/4:...Prepare similar candidates for each seed word in the word2vec model...') - self.__search(seedword_txt_file) - for idx in self.similars_candidate_idxs: - score = self.model.wv.n_similarity([idx], self.seedidxs) - simidx_scores.append((idx, score)) - simidxs = [w[0] for w in sorted(simidx_scores, key=lambda k:k[1], reverse=True)] - - simwords = [str(self.model.wv.index_to_key[idx]) for idx in simidxs][:topn] - - resultwords = [] - resultwords.extend(seedwords) - resultwords.extend(simwords) - - txtdir = Path(self.cwd).joinpath('output', 'w2v_candi_words') - Path(self.cwd).joinpath('output', 'w2v_candi_words').mkdir(exist_ok=True) - candidatetxtfile = Path(txtdir).joinpath('{}.txt'.format(seedwordsname)) - with open(candidatetxtfile, 'w', encoding='utf-8') as f: - for word in resultwords: - f.write(word+'\n') - duration = int(time.time()-self.start) - duration = round(duration, 2) - print('Step 4/4 Finish! Used {duration} s'.format(duration=duration)) - - - -def co_occurrence_matrix(documents, window_size=2, lang='chinese'): - """ - Build a co-word matrix - :param documents: a list of documents - :param window_size: Window size, the default value is set to 2 - :param lang: Language type, the default setting is "chinese" - :return: - """ - d = defaultdict(int) - vocab = set() - if lang == 'english': - for document in documents: - document = document.lower().split() - for i in range(len(document)): - token = document[i] - vocab.add(token) # add to vocab - next_token = document[i + 1: i + 1 + window_size] - for t in next_token: - key = tuple(sorted([t, token])) - d[key] += 1 - - elif lang =='chinese': - for document in documents: - document = list(jieba.cut(document)) - # iterate over sentences - for i in range(len(list(document))): - token = document[i] - vocab.add(token) # add to vocab - next_token = document[i + 1: i + 1 + window_size] - for t in next_token: - key = tuple(sorted([t, token])) - d[key] += 1 - - # formulate the dictionary into dataframe - vocab = sorted(vocab) # sort vocab - df = pd.DataFrame(data=np.zeros((len(vocab), len(vocab)), dtype=np.int16), - index=vocab, - columns=vocab) - for key, value in d.items(): - df.at[key[0], key[1]] = value - df.at[key[1], key[0]] = value - return df - - - - - - - - -class Glove(object): - def __init__(self, cwd, lang='chinese'): - """ - initialize the Glove model - - :param lang: set language for Glove model - :return: - """ - self.lang=lang - self.cwd = cwd - - - - def create_vocab(self, file, min_count=5): - """ - create vocabulary - - :param file: corpus file path, only support .txt file now! - :params min_count: When building the vocabulary ignore terms that wordcount strictly higher than the given threshold - :return: - """ - self.glove_output_name = file.split('/')[-1].replace('.txt', '') - print('Step 1/4: ...Create vocabulary for Glove.') - text = open(file, encoding='utf-8').read() - if self.lang=='chinese': - words = list(jieba.cut(text)) - self.words = [w for w in words if w not in STOPWORDS_zh] - else: - words = text.split(' ') - self.words = [w.lower() for w in words if w not in STOPWORDS_en] - # 压缩vocab - self.vocab = [k for (k, v) in Counter(self.words).items() if v >= min_count] - return self.vocab - - - def cooccurrence_matrix(self): - """ - Create cooccurrence matrix - - :return: - """ - print('Step 2/4: ...Create cooccurrence matrix.') - cv = CountVectorizer(ngram_range=(1, 1), - vocabulary=self.vocab) - - docs = [' '.join(self.words)] - X = cv.fit_transform(docs) - Xc = (X.T * X) - Xc.setdiag(0) - self.coo_matrix = Xc.toarray() - return self.coo_matrix - - - def train_embeddings(self, vector_size=50, max_iter=25): - """ - Train glove embeddings - - :params vector_size: Dimensionality of the word vectors. Default: 50 - :params max_iter: Number of training epochs. Default: 25 - :return: - """ - start = time.time() - print('Step 3/4: ...Train glove embeddings. \n Note, this part takes a long time to run') - glove_model = GloVe(n=vector_size, max_iter=max_iter) - embeddings = glove_model.fit(self.coo_matrix) - self.glove_embeddings = np.column_stack((np.array(self.vocab), embeddings)) - - end = time.time() - duration = round(end-start, 2) - print('Step 3/4: ... Finish! Use {} s'.format(duration)) - return self.glove_embeddings - - - def save(self, model_name='glove_model'): - """ - Save glove embeddings as a txt file. Note we will use gensim to convert the glove embeddings in word2vec format - - :params model_name: txt file name; save the glove model to txt file. - :return: - """ - - print('Step 4/4: ... Save the glove embeddings to a txt file') - from gensim.scripts.glove2word2vec import glove2word2vec - txtdir = Path(self.cwd).joinpath('output', 'Glove') - Path(self.cwd).joinpath('output', 'Glove').mkdir(exist_ok=True) - glove_file = Path(txtdir).joinpath('{}.txt'.format(model_name)) - with open(glove_file, 'a+', encoding='utf-8') as f: - res_text = '' - for glove_embedding in self.glove_embeddings: - res_text += ' '.join(glove_embedding) + '\n' - f.write(res_text) - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/build/lib/cntext/files/ADV_CONJ.pkl b/build/lib/cntext/files/ADV_CONJ.pkl deleted file mode 100644 index c532c6e6f9be1cd646dec708a571cbefd7820434..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2343 zcmX|D%Wfl85DmIROA%X^z?u~c{s4hkuz(#q*o|bdXNlBy;i?qSLB-{U_B@{L8@P`dho zve^-QFLIl5BB!+wI7_Rdk<<2d+z7HH)K&aR;HEEyX1C!%UfwUjV%Gz6RVi%VJp$PD z6h8nHro}yJRbC0q#;L8}^11dMWw=*3j{5+5Rn?z>-F&7&^C@nu#icKBI7_m58&-LL 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chardet.UniversalDetector() - for line in f: - detector.feed(line) - if detector.done: - break - num_lines -= 1 - if num_lines == 0: - break - detector.close() - return detector.result['encoding'] - - - -def read_file(file, encoding='utf-8', **kwargs): - """ - Read data from common format file, support .txt, .csv, .pdf, .docx, .json, .dta, etc. - - Args: - file (str): file path - encoding (str, optional): Defaults to 'utf-8'. - **kwargs: other arguments for pd.read_csv, pd.read_excel, pd.read_stata, pd.read_json, pdfdocx.read_pdf, pdfdocx.read_docx - - Returns: - DataFrame - """ - if '.txt' in file or '.TXT' in file: - text = open(file, 'r', encoding=encoding, **kwargs).read() - elif '.docx' in file or '.DOCX' in file: - text = read_docx(file) - elif '.pdf' in file or '.PDF' in file: - text = read_pdf(file, **kwargs) - elif '.xls' in file or '.xlsx' in file: - text = pd.read_excel(file,**kwargs) - elif '.csv' in file or '.CSV' in file: - text = pd.read_csv(file, encoding=encoding, **kwargs) - elif '.dta' in file or '.DTA' in file: - text = pd.read_stata(file, **kwargs) - elif '.json' in file or '.JSON' in file: - text = pd.read_json(file, encoding=encoding, **kwargs) - else: - print('无能为力') - text = pd.DataFrame(dict()) - - if type(text)!=pd.DataFrame: - df = pd.DataFrame({ - 'doc': text, - 'file': file - }, index=[0]) - else: - df = text - - return df - - - -def get_files(fformat='*.txt', recursive=True): - """ - Get a list of file path in a folder - - Args: - fformat (str): filter files, the default value is '*.txt', which means this function only returns a file path list of TXT files. Defaults to '*.txt'. Other options are '*.pdf', '*.docx', '*.csv', '*.xls', '*.xlsx', '*.txt' .If you dont know the subdirectory of the folder, you can use '**/*.txt', - '**/*.pdf', '**/*.docx', '**/*.csv', '**/*.xls', '**/*.xlsx', '**/*.txt' - recursive (bool, optional): Whether to recursive search in folder. Defaults to True. - - Returns: - a list of file path - """ - file_list = glob.glob(fformat, recursive=recursive) - - #unify the sep - file_list = [file_path.replace('\\', '/') for file_path in file_list] - return file_list - - - -def read_files(fformat='*.*', encoding='utf-8', recursive=True, **kwargs): - """ - Read files from specificed folder path. - - Args: - fformat (str): filter files, the default value is '*.txt', which means this function only returns a file path list of TXT files. Defaults to '*.txt'. Other options are '*.pdf', '*.docx', '*.csv', '*.xls', '*.xlsx', '*.txt' , '*.dta', '*.json' .If you dont know the subdirectory of the folder, you can use '*/*.txt', - '*/*.pdf', '*/*.docx', '*/*.csv', '*/*.xls', '*/*.xlsx', '*/*.txt' , '*/*.dta' , '*/*.json' - - recursive (bool, optional): Whether to recursive search in folder. Defaults to True. - - Returns: - DataFrame - """ - dfs = [] - files = get_files(fformat=fformat, recursive=recursive) - for file in files: - try: - dfs.append(read_file(file, encoding=encoding, **kwargs)) - except: - pass - all_df = pd.concat(dfs, axis=0, ignore_index=True) - return all_df \ No newline at end of file diff --git a/build/lib/cntext/mind.py b/build/lib/cntext/mind.py deleted file mode 100644 index 94aa284..0000000 --- a/build/lib/cntext/mind.py +++ /dev/null @@ -1,207 +0,0 @@ -import numpy as np -from numpy import dot -from numpy.linalg import norm -from time import time -from gensim.models.keyedvectors import KeyedVectors -import scipy.spatial.distance -import itertools - -class Text2Mind(object): - """ - Calculate cognitive (attitude, bias) direction and strength in text - """ - def __init__(self, w2v_model_path='glove_w2v.6B.100d.txt'): - """ - Init the Text2Mind - :param w2v_model_path: pretained embedding model file path, only support word2vec format pretained model! - """ - print('Loading the model of {}'.format(w2v_model_path)) - start = time() - try: - self.model = KeyedVectors.load_word2vec_format(w2v_model_path, binary=False, no_header=True) - except: - self.model = KeyedVectors.load_word2vec_format(w2v_model_path) - duration = round(time()-start, 2) - print('Load successfully, used {} s'.format(duration)) - - def k2v_model(self, word): - """ - return the KeyedVectors object. - """ - return self.model - - - def get_vector(self, word): - """ - get word vector - """ - return self.model.get_vector(word) - - - - def __get_centroid(self, words): - """ - calculate the centroid vector of multiple word vectors - :param words: word list - :return: - """ - container = np.zeros(self.model.vector_size) - for word in words: - try: - container = container + self.model.get_vector(word) - except: - assert "No word in embeddings models" - return container / len(words) - - - def sematic_projection(self, words, c_words1, c_words2): - """ - Calculate the projected length of each word in the concept vector.Note that the calculation result reflects the direction of concept. - Greater than 0 means semantically closer to c_words2. - - Refer "Grand, G., Blank, I.A., Pereira, F. and Fedorenko, E., 2022. Semantic projection recovers rich human knowledge of multiple object features from word embeddings. _Nature Human Behaviour_, pp.1-13." - - For example, in the size concept, if you want positive means big, and negative means small, - you should set c_words1 = ["small", "little", "tiny"] c_words2 = ["large", "big", "huge"]. - - :param words: words list - :param c_words1: concept words1, c_words1 = ["small", "little", "tiny"] - :param c_words2: concept words2, c_words2 = ["large", "big", "huge"] - :param c_vector: concept_vector; the result of .build_concept(c_words1, c_words2) - :return: - """ - container = [] - source_vector = self.__get_centroid(c_words1) - target_vector = self.__get_centroid(c_words2) - c_vector = target_vector - source_vector - concept_norm = norm(c_vector) - for word in words: - any_vector = self.model.get_vector(word) - projection_score = dot(any_vector, c_vector) / concept_norm - projection_score = round(projection_score, 2) - container.append((word, projection_score)) - container = sorted(container, key=lambda k: k[1], reverse=False) - return container - - - def sematic_distance(self, words, c_words1, c_words2): - """ - Calculate the distance from words with c_words1 and c_words2 respectively, and return the difference between the two distance. - Greater than 0 means semantically closer to c_words2 - - :param words: words list, words = ['program', 'software', 'computer'] - :param c_words1: concept words1, c_words1 = ["man", "he", "him"] - :param c_words2: concept words2, c_words2 = ["woman", "she", "her"] - :return: - """ - any_vector = self.__get_centroid(words) - c_vector1 = self.__get_centroid(c_words1) - c_vector2 = self.__get_centroid(c_words2) - dist_1 = np.linalg.norm(any_vector - c_vector1) - dist_2 = np.linalg.norm(any_vector - c_vector2) - res = dist_1-dist_2 - return round(res, 2) - - - def divergent_association_task(words, minimum=7): - """Compute DAT score, get the detail of algorithm, please refer to Olson, J. A., Nahas, J., Chmoulevitch, D., Cropper, S. J., & Webb, M. E. (2021). Naming unrelated words predicts creativity. Proceedings of the National Academy of Sciences, 118(25), e2022340118.""" - # Keep only valid unique words - uniques = [] - for word in words: - try: - self.model.get_vector(word) - uniques.append(word) - except: - pass - - - # Keep subset of words - if len(uniques) >= minimum: - subset = uniques[:minimum] - else: - return None # Not enough valid words - - # Compute distances between each pair of words - distances = [] - for word1, word2 in itertools.combinations(subset, 2): - dist = scipy.spatial.distance.cosine(self.model.get_vector(word1), self.model.get_vector(word2)) - distances.append(dist) - - # Compute the DAT score (average semantic distance multiplied by 100) - return (sum(distances) / len(distances)) * 100 - - - - - -# class Alignment(object): -# def __init__(self): -# pass - - - -# def procrustes(A, B): -# """ -# Learn the best rotation matrix to align matrix B to A -# https://en.wikipedia.org/wiki/Orthogonal_Procrustes_problem -# """ -# # U, _, Vt = np.linalg.svd(B.dot(A.T)) -# U, _, Vt = np.linalg.svd(B.T.dot(A)) -# return U.dot(Vt) - -# def intersect_vocab (idx1, idx2): -# """ Intersect the two vocabularies - -# Parameters: -# =========== -# idx1 (dict): the mapping for vocabulary in the first group -# idx2 (dict): the mapping for vocabulary in the second group - -# Returns: -# ======== -# common_idx, common_iidx (tuple): the common mapping for vocabulary in both groups -# """ -# common = idx1.keys() & idx2.keys() -# common_vocab = [v for v in common] - -# common_idx, common_iidx = {v:i for i,v in enumerate (common_vocab)}, {i:v for i,v in enumerate (common_vocab)} -# return common_vocab, (common_idx, common_iidx) - -# def align_matrices (mat1, mat2, idx1, idx2): -# """ Align the embedding matrices and their vocabularies. - -# Parameters: -# =========== -# mat1 (numpy.ndarray): embedding matrix for first group -# mat2 (numpy.ndarray): embedding matrix for second group - -# index1 (dict): the mapping dictionary for first group -# index2 (dict): the mapping dictionary for the second group - -# Returns: -# ======== -# remapped_mat1 (numpy.ndarray): the aligned matrix for first group -# remapped_mat2 (numpy.ndarray): the aligned matrix for second group -# common_vocab (tuple): the mapping dictionaries for both the matrices -# """ -# common_vocab, (common_idx, common_iidx) = intersect_vocab (idx1, idx2) -# row_nums1 = [idx1[v] for v in common_vocab] -# row_nums2 = [idx2[v] for v in common_vocab] - -# #print (len(common_vocab), len (common_idx), len (common_iidx)) -# remapped_mat1 = mat1[row_nums1, :] -# remapped_mat2 = mat2[row_nums2, :] -# #print (mat1.shape, mat2.shape, remapped_mat1.shape, remapped_mat2.shape) - -# omega = procrustes (remapped_mat1, remapped_mat2) -# #print (omega.shape) -# # rotated_mat2 = np.dot (omega, remapped_mat2) -# rotated_mat2 = np.dot (remapped_mat2, omega) - -# return remapped_mat1, rotated_mat2, (common_idx, common_iidx) - - - - - - diff --git a/build/lib/cntext/similarity.py b/build/lib/cntext/similarity.py deleted file mode 100644 index ceb47bb..0000000 --- a/build/lib/cntext/similarity.py +++ /dev/null @@ -1,114 +0,0 @@ -#!/usr/bin/python -# -*- coding: utf-8 -*- -from sklearn.feature_extraction.text import CountVectorizer -from sklearn.metrics.pairwise import cosine_similarity -from difflib import Differ, SequenceMatcher -import jieba -#from math import * -import warnings - -warnings.filterwarnings('ignore') - - -def __check_contain_chinese(check_str): - """ - check language, it will be True when check_str is chinese character. - :param check_str: - :return: - """ - for ch in check_str: - if u'\u4e00' <= ch <= u'\u9fff': - return True - return False - - -def __transform(text1, text2): - """ - Convert text1, text2 to vector vec1, vec2 - :param text1: - :param text2: - :return: - """ - - if __check_contain_chinese(text1): - text1 = ' '.join(list(jieba.cut(text1))) - text2 = ' '.join(list(jieba.cut(text2))) - else: - text1 = text1.lower() - text2 = text2.lower() - - corpus = [text1, text2] - cv = CountVectorizer(binary=True) - cv.fit(corpus) - vec1 = cv.transform([text1]).toarray() - vec2 = cv.transform([text2]).toarray() - return text1, text2, vec1, vec2 - - -#def cosine_similarity(vec1, vec2): -# cos_sim = cosine_similarity(vec1, vec2)[0][0] -# return cos_sim[0][0] - -def jaccard_sim(text1, text2): - """ - jaccard similarity algorithm; - :param text1: text string - :param text2: text string - :return: - """ - text11, text22, vec1, vec2 = __transform(text1, text2) - """ returns the jaccard similarity between two lists """ - vec1 = set([idx for idx, v in enumerate(vec1[0]) if v > 0]) - vec2 = set([idx for idx, v in enumerate(vec2[0]) if v > 0]) - res = len(vec1 & vec2) / len(vec1 | vec2) - return format(res, '.2f') - -def minedit_sim(text1, text2): - """ - Minimum edit distance similarity algorithm; - :param text1: text string - :param text2: text string - :return: - """ - words1 = list(jieba.cut(text1.lower())) - words2 = list(jieba.cut(text2.lower())) - leven_cost = 0 - s = SequenceMatcher(None, words1, words2) - for tag, i1, i2, j1, j2 in s.get_opcodes(): - if tag == 'replace': - leven_cost += max(i2 - i1, j2 - j1) - elif tag == 'insert': - leven_cost += (j2 - j1) - elif tag == 'delete': - leven_cost += (i2 - i1) - return format(leven_cost, '.2f') - - -def simple_sim(text1, text2): - """ - simple similarity algorithm; - Refer to the track change function in Microsoft Word and the diff function in Unix systems. - :param text1: text string - :param text2: text string - :return: - """ - words1 = list(jieba.cut(text1.lower())) - words2 = list(jieba.cut(text2.lower())) - diff = Differ() - diff_manipulate = list(diff.compare(words1, words2)) - c = len(diff_manipulate) / (len(words1) + len(words2)) - cmax = max([len(words1), len(words2)]) - res = (cmax - c) / cmax - return format(res, '.2f') - - -def cosine_sim(text1, text2): - """ - cosine similarity algorithm; - :param text1: text string - :param text2: text string - :return: - """ - text11, text22, vec1, vec2 = __transform(text1, text2) - res = cosine_similarity(vec1, vec2)[0][0] - return format(res, '.2f') diff --git a/build/lib/cntext/stats.py b/build/lib/cntext/stats.py deleted file mode 100644 index 551615f..0000000 --- a/build/lib/cntext/stats.py +++ /dev/null @@ -1,294 +0,0 @@ -import re -import jieba -from collections import Counter -import numpy as np -import pathlib -import pickle -import os - - - -def cn_seg_sent(text): - #split the chinese text into sentences - text = re.sub('([。!;?;\?])([^”’])', "[[end]]", text) # 单字符断句符 - text = re.sub('([。!?\?][”’])([^,。!?\?])', "[[end]]", text) - text = re.sub('\s', '', text) - # 如果双引号前有终止符,那么双引号才是句子的终点,把分句符\n放到双引号后,注意前面的几句都小心保留了双引号 - return text.split("[[end]]") - - -def load_pkl_dict(file, is_builtin=True): - """ - load pkl dictionary file, - :param file: pkl file path - :param is_builtin: Whether it is the built-in pkl file of cntext. Default True - """ - if is_builtin==True: - pathchain = ['files',file] - dict_file_path = pathlib.Path(__file__).parent.joinpath(*pathchain) - dict_f = open(dict_file_path, 'rb') - else: - dict_f = open(file, 'rb') - dict_obj = pickle.load(dict_f) - return dict_obj - - - - -def dict_pkl_list(): - """ - Get the list of cntext built-in dictionaries (pkl format) - """ - dict_file_path = pathlib.Path(__file__).parent.joinpath('files') - return [f for f in os.listdir(dict_file_path) if 'pkl' in f] - - - - -STOPWORDS_zh = load_pkl_dict(file='STOPWORDS.pkl')['STOPWORDS']['chinese'] -STOPWORDS_en = load_pkl_dict(file='STOPWORDS.pkl')['STOPWORDS']['english'] -ADV_words = load_pkl_dict(file='ADV_CONJ.pkl')['ADV'] -CONJ_words = load_pkl_dict(file='ADV_CONJ.pkl')['CONJ'] - - - -def term_freq(text, lang='chinese'): - """ - Calculate word count - :param text: text string - :param language: "chinese" or "english"; default is "chinese" - """ - if lang=='chinese': - #text = ''.join(re.findall('[\u4e00-\u9fa5]+', text)) - words = list(jieba.cut(text)) - words = [w for w in words if w not in STOPWORDS_zh] - else: - words = text.lower().split(" ") - words = [w for w in words if w not in STOPWORDS_en] - return Counter(words) - - - -def readability(text, zh_advconj=None, lang='chinese'): - """ - text readability, the larger the indicator, the higher the complexity of the article and the worse the readability. - :param text: text string - :param zh_advconj Chinese conjunctions and adverbs, receive list data type. By default, the built-in dictionary of cntext is used - :param language: "chinese" or "english"; default is "chinese" - ------------ - 【English readability】english_readability = 4.71 x (characters/words) + 0.5 x (words/sentences) - 21.43; - 【Chinese readability】 Refer 【徐巍,姚振晔,陈冬华.中文年报可读性:衡量与检验[J].会计研究,2021(03):28-44.】 - readability1 ---每个分句中的平均字数 - readability2 ---每个句子中副词和连词所占的比例 - readability3 ---参考Fog Index, readability3=(readability1+readability2)×0.5 - 以上三个指标越大,都说明文本的复杂程度越高,可读性越差。 - - """ - if lang=='english': - text = text.lower() - #将浮点数、整数替换为num - text = re.sub('\d+\.\d+|\.\d+', 'num', text) - num_of_characters = len(text) - #英文分词 - rgx = re.compile("(?:(?:[^a-zA-Z]+')|(?:'[^a-zA-Z]+))|(?:[^a-zA-Z']+)") - num_of_words = len(re.split(rgx, text)) - #分句 - num_of_sentences = len(re.split('(?=3.5 -Description-Content-Type: text/markdown -License-File: LICENSE - -[![DOI](https://zenodo.org/badge/487297608.svg)](https://zenodo.org/badge/latestdoi/487297608) - - - - -**Table of Contents** - -- [Installation](#installation) -- [QuickStart](#quickstart) -- [1. Basic](#1-basic) - - [1.1 readability](#11--readability) - - [1.2 term_freq(text, lang)](#12--term_freqtext-lang) - - [1.3 dict_pkl_list](#13-dict_pkl_list) - - [1.4 load_pkl_dict](#14-load_pkl_dict) - - [1.5 sentiment](#15-sentiment) - - [1.6 sentiment_by_valence()](#16-sentiment_by_valence) -- [2. dictionary](#2-dictionary) - - [2.1 SoPmi](#21-sopmi) - - [2.2 W2VModels](#22-w2vmodels) - - [Note](#note) - - [2.3 co_occurrence_matrix](#23-co_occurrence_matrix) - - [2.4 Glove](#24--glove) -- [3. similarity](#3-similarity) -- [4. Text2Mind](#4-text2mind) - - [4.1 tm.sematic_distance(words, c_words1, c_words2)](#41-tmsematic_distancewords-c_words1-c_words2) - - [4.2 tm.sematic_projection(words, c_words1, c_words2)](#42-tmsematic_projectionwords-c_words1-c_words2) -- [Citation](#citation) - - [apalike](#apalike) - - [bibtex](#bibtex) - - [endnote](#endnote) - - - - -![](img/logo.png) - - - -[中文文档](chinese_readme.md) - -[中文博客](https://hidadeng.github.io/blog/) - -**cntext** is a text analysis package that provides traditional text analysis methods, such as word count, readability, document similarity, sentiment analysis, etc. It has built-in multiple Chinese and English sentiment dictionaries. Supporting word embedding models training and usage, cntext provides semantic distance and semantic projection now. - -- [github repo](https://github.com/hidadeng/cntext) ``https://github.com/hidadeng/cntext`` - -- [pypi link](https://pypi.org/project/cntext/) ``https://pypi.org/project/cntext/`` - - - - - - -
- -## Installation - -``` -pip install cntext --upgrade -pip install numpy==1.24.2 -pip install gensim==4.2.0 -pip install scikit-learn==1.1.2 -``` - - - -
- -## QuickStart - -```python -import cntext as ct - -help(ct) -``` - -Run - -``` -Help on package cntext: - -NAME - cntext - -PACKAGE CONTENTS - bias - dictionary - similarity - stats -``` - - - -
- - - -## 1. Basic - -Currently, the built-in functions of stats.py are: - -- **readability()** the readability of text, support Chinese and English -- **term_freq()** word count -- **dict_pkl_list()** get the list of built-in dictionaries (pkl format) in cntext -- **load_pkl_dict()** load the pkl dictionary file -- **sentiment()** sentiment analysis -- **sentiment_by_valence()** valence sentiment analysis - - - -```python -import cntext as ct - -text = 'What a sunny day!' - - -diction = {'Pos': ['sunny', 'good'], - 'Neg': ['bad', 'terrible'], - 'Adv': ['very']} - -ct.sentiment(text=text, - diction=diction, - lang='english') -``` - -Run - -``` -{'Pos_num': 1, - 'Neg_num': 0, - 'Adv_num': 0, - 'stopword_num': 1, - 'word_num': 5, - 'sentence_num': 1} -``` - -
- - - -### 1.1 readability - -The larger the indicator, the higher the complexity of the article and the worse the readability. - -**readability(text, lang='chinese')** - -- text: text string -- lang: "chinese" or "english",default is "chinese" - - - -```python -import cntext as ct - -text = 'Committed to publishing quality research software with zero article processing charges or subscription fees.' - -ct.readability(text=text, - lang='english') -``` - -Run - -``` -{'readability': 19.982} -``` - -
- - - -### 1.2 term_freq(text, lang) - -Word count statistics function, return Counter type. - -```python -import cntext as ct - -text = 'Committed to publishing quality research software with zero article processing charges or subscription fees.' - -ct.term_freq(text=text, lang='english') -``` - -Run - -``` -Counter({'committed': 1, - 'publishing': 1, - 'quality': 1, - 'research': 1, - 'software': 1, - 'zero': 1, - 'article': 1, - 'processing': 1, - 'charges': 1, - 'subscription': 1, - 'fees.': 1}) -``` - -
- - - -### 1.3 dict_pkl_list - -get the list of built-in dictionaries (pkl format) in cntext - -```python -import cntext as ct - -ct.dict_pkl_list() -``` - -Run - -``` -['DUTIR.pkl', - 'HOWNET.pkl', - 'sentiws.pkl', - 'Chinese_Digitalization.pkl', - 'ChineseFinancialFormalUnformalSentiment.pkl', - 'Concreteness.pkl', - 'ANEW.pkl', - 'LSD2015.pkl', - 'NRC.pkl', - 'geninqposneg.pkl', - 'HuLiu.pkl', - 'AFINN.pkl', - 'ChineseEmoBank.pkl', - 'ADV_CONJ.pkl', - 'Loughran_McDonald_Financial_Sentiment.pkl', - 'Chinese_Loughran_McDonald_Financial_Sentiment.pkl', - 'STOPWORDS.pkl'] -``` - -We list 12 pkl dictionary here, some of English dictionary listed below are organized from [quanteda.sentiment](https://github.com/quanteda/quanteda.sentiment) - -| pkl文件 | 词典 | 语言 | 功能 | -| ------------------------------------------- | ------------------------------------------------------------ | --------------- | ------------------------------------------------------------ | -| ChineseEmoBank.pkl | Chinese Sentiment Dictionary, includes 「valence」「arousal」. In cntext, we only take Chinese valence-arousal words (CVAW, single word) into account, ignore CVAP, CVAS, CVAT. | Chinese | valence, arousal| -| DUTIR.pkl | DUTIR | Chinese | Seven categories of emotions: 哀, 好, 惊, 惧, 乐, 怒, 恶 | -| HOWNET.pkl | Hownet | Chinese | Positive、Negative | -| SentiWS.pkl | SentimentWortschatz (SentiWS) | German | Positive、Negative;
| -| ChineseFinancialFormalUnformalSentiment.pkl | Chinese finance dictionary, contains formal、unformal、positive、negative | Chinese | formal-pos、
formal-neg;
unformal-pos、
unformal-neg | -| ANEW.pkl | Affective Norms for English Words (ANEW) | English | | -| LSD2015.pkl | Lexicoder Sentiment Dictionary (2015) | English | Positive、Negative | -| NRC.pkl | NRC Word-Emotion Association Lexicon | English | fine-grained sentiment words; | -| HuLiu.pkl | Hu&Liu (2004) | English | Positive、Negative | -| AFINN.pkl | Affective Norms for English Words | English | | -| ADV_CONJ.pkl | adverbial & conjunction | Chinese | | -| STOPWORDS.pkl | | English&Chinese | stopwordlist | -| Concreteness.pkl | Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46, 904–911 | English | word & concreateness score | -| Chinese_Loughran_McDonald_Financial_Sentiment.pkl | 曾庆生, 周波, 张程, and 陈信元. "年报语调与内部人交易: 表里如一还是口是心非?." 管理世界 34, no. 09 (2018): 143-160. | Chinese | 正面、负面词 | -| Chinese_Digitalization.pkl |吴非,胡慧芷,林慧妍,任晓怡. 企业数字化转型与资本市场表现——来自股票流动性的经验证据[J]. 管理世界,2021,37(07):130-144+10. | 中文 | 基于这篇论文,构建了中文数字化词典,含人工智能技术、大数据技术、云计算技术、区块链技术、数字技术应用等关键词列表。 | -| Loughran_McDonald_Financial_Sentiment.pkl | Loughran, Tim, and Bill McDonald. "When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks." The Journal of finance 66, no. 1 (2011): 35-65. | English | Positive and Negative emotion words in the financial field。 Besides, in version of 2018, author add ``Uncertainty, Litigious, StrongModal, WeakModal, Constraining`` | -| Chinese_FLS.pkl | 许帅,邵帅,何贤杰.业绩说明会前瞻性信息对分析师盈余预测准确性的影响——信口雌黄还是言而有征[J].中国管理科学:1-15. | 中文 | 前瞻性词典集,含174个词语 | - - - - - - -
- -### 1.4 load_pkl_dict - -load the pkl dictionary file and return dict type data. - -```python -import cntext as ct - -print(ct.__version__) -# load the pkl dictionary file -print(ct.load_pkl_dict('NRC.pkl')) -``` - -Run - -``` -1.8.0 - -{'NRC': {'anger': ['abandoned', 'abandonment', 'abhor', 'abhorrent', ...], - 'anticipation': ['accompaniment','achievement','acquiring', ...], - 'disgust': ['abject', 'abortion', 'abundance', 'abuse', ...], - 'fear': ['anxiety', 'anxious', 'apache', 'appalling', ...], - ...... - - 'Desc': 'NRC Word-Emotion Association Lexicon', - 'Referer': 'Mohammad, Saif M., and Peter D. Turney. "Nrc emotion lexicon." National Research Council, Canada 2 (2013).' - } -``` - -
- - - -### 1.5 sentiment - -**sentiment(text, diction, lang='chinese')** - -Calculate the occurrences of each emotional category words in text; The complex influence of adverbs and negative words on emotion is not considered. - -- **text**: text string -- **diction**: emotion dictionary data, support diy or built-in dicitonary -- **lang**: "chinese" or "english",default is "chinese" - - - -We can use built-in dicitonary in cntext, such as NRC.pkl - -```python -import cntext as ct - -text = 'What a happy day!' - -ct.sentiment(text=text, - diction=ct.load_pkl_dict('NRC.pkl')['NRC'], - lang='english') -``` - -Run - -``` -{'anger_num': 0, - 'anticipation_num': 1, - 'disgust_num': 0, - 'fear_num': 0, - 'joy_num': 1, - 'negative_num': 0, - 'positive_num': 1, - 'sadness_num': 0, - 'surprise_num': 0, - 'trust_num': 1, - 'stopword_num': 1, - 'word_num': 5, - 'sentence_num': 1} -``` - -We can also use DIY dicitonary, just like - -```python -import cntext as ct - -text = 'What a happy day!' - -diction = {'Pos': ['happy', 'good'], - 'Neg': ['bad', 'terrible'], - 'Adv': ['very']} - -ct.sentiment(text=text, - diction=diction, - lang='english') -``` - -Run - -``` -{'Pos_num': 1, - 'Neg_num': 0, - 'Adv_num': 0, - 'stopword_num': 1, - 'word_num': 5, - 'sentence_num': 1} -``` - -
- - - -### 1.6 sentiment_by_valence() - -**sentiment_by_valence(text, diction, lang='english')** - -Calculate the occurrences of each sentiment category words in text; The complex influence of intensity adverbs and negative words on emotion is not considered. - -- text: text sring -- diction: sentiment dictionary with valence.; -- lang: "chinese" or "english"; default language="english" - - - -Here we want to study the concreteness of text. The **concreteness.pkl** that comes from Brysbaert2014. - ->Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46, 904–911 - -```python -import cntext as ct - -# load the concreteness.pkl dictionary file; cntext version >=1.7.1 -concreteness_df = ct.load_pkl_dict('concreteness.pkl')['concreteness'] -concreteness_df.head() -``` - -Run - -|| word | valence | -| ---: | :-------------- | ----------: | -| 0 | roadsweeper | 4.85 | -| 1 | traindriver | 4.54 | -| 2 | tush | 4.45 | -| 3 | hairdress | 3.93 | -| 4 | pharmaceutics | 3.77 | - -
- -```python -reply = "I'll go look for that" - -score=ct.sentiment_by_valence(text=reply, - diction=concreteness_df, - lang='english') -score -``` - -Run - -``` -1.85 -``` - - - -
- -```python -employee_replys = ["I'll go look for that", - "I'll go search for that", - "I'll go search for that top", - "I'll go search for that t-shirt", - "I'll go look for that t-shirt in grey", - "I'll go search for that t-shirt in grey"] - -for idx, reply in enumerate(employee_replys): - score=ct.sentiment_by_valence(text=reply, - diction=concreteness_df, - lang='english') - - template = "Concreteness Score: {score:.2f} | Example-{idx}: {exmaple}" - print(template.format(score=score, - idx=idx, - exmaple=reply)) - -ct.sentiment_by_valence(text=text, diction=concreteness_df, lang='english') -``` - -Run - -``` -Concreteness Score: 1.55 | Example-0: I'll go look for that -Concreteness Score: 1.55 | Example-1: I'll go search for that -Concreteness Score: 1.89 | Example-2: I'll go search for that top -Concreteness Score: 2.04 | Example-3: I'll go search for that t-shirt -Concreteness Score: 2.37 | Example-4: I'll go look for that t-shirt in grey -Concreteness Score: 2.37 | Example-5: I'll go search for that t-shirt in grey -``` - - - -

- - - - - -## 2. dictionary - -This module is used to build or expand the vocabulary (dictionary), including - -- **SoPmi** Co-occurrence algorithm to extend vocabulary (dictionary), Only support chinese -- **W2VModels** using word2vec to extend vocabulary (dictionary), support english & chinese - -### 2.1 SoPmi - -```python -import cntext as ct -import os - -sopmier = ct.SoPmi(cwd=os.getcwd(), - #raw corpus data,txt file.only support chinese data now. - input_txt_file='data/sopmi_corpus.txt', - #muanually selected seed words - seedword_txt_file='data/sopmi_seed_words.txt', #人工标注的初始种子词 - ) - -sopmier.sopmi() -``` - -Run - -``` -Step 1/4:...Preprocess Corpus ... -Step 2/4:...Collect co-occurrency information ... -Step 3/4:...Calculate mutual information ... -Step 4/4:...Save candidate words ... -Finish! used 44.49 s -``` - - - -
- -### 2.2 W2VModels - -**In particular, note that the code needs to set the lang parameter** - -```python -import cntext as ct -import os - -#init W2VModels, corpus data w2v_corpus.txt -model = ct.W2VModels(cwd=os.getcwd(), lang='english') -model.train(input_txt_file='data/w2v_corpus.txt') - - -#According to the seed word, filter out the top 100 words that are most similar to each category words -model.find(seedword_txt_file='data/w2v_seeds/integrity.txt', - topn=100) -model.find(seedword_txt_file='data/w2v_seeds/innovation.txt', - topn=100) -model.find(seedword_txt_file='data/w2v_seeds/quality.txt', - topn=100) -model.find(seedword_txt_file='data/w2v_seeds/respect.txt', - topn=100) -model.find(seedword_txt_file='data/w2v_seeds/teamwork.txt', - topn=100) -``` - -Run - -``` -Step 1/4:...Preprocess corpus ... -Step 2/4:...Train word2vec model - used 174 s -Step 3/4:...Prepare similar candidates for each seed word in the word2vec model... -Step 4/4 Finish! Used 187 s -Step 3/4:...Prepare similar candidates for each seed word in the word2vec model... -Step 4/4 Finish! Used 187 s -Step 3/4:...Prepare similar candidates for each seed word in the word2vec model... -Step 4/4 Finish! Used 187 s -Step 3/4:...Prepare similar candidates for each seed word in the word2vec model... -Step 4/4 Finish! Used 187 s -Step 3/4:...Prepare similar candidates for each seed word in the word2vec model... -Step 4/4 Finish! Used 187 s - -``` - -
- -### Note - -When runing out the W2VModels, there will appear a file called **w2v.model** in the directory of **output/w2v_candi_words**.Note this w2v file can be used later. - -```python -from gensim.models import KeyedVectors - -w2v_model = KeyedVectors.load("the path of w2v.model") -#to extract vector for word -#w2v_model.get_vector(word) -#if you need more information about the usage of w2_model, please use help function -#help(w2_model) -``` - -For example, we load the ``output/w2v_candi_words/w2v.model`` - -```python -from gensim.models import KeyedVectors - -w2v_model = KeyedVectors.load('output/w2v_candi_words/w2v.model') -# find the most similar word in w2v.model -w2v_model.most_similar('innovation') -``` - -Run - -``` -[('technology', 0.689210832118988), - ('infrastructure', 0.669672966003418), - ('resources', 0.6695448160171509), - ('talent', 0.6627111434936523), - ('execution', 0.6549549102783203), - ('marketing', 0.6533523797988892), - ('merchandising', 0.6504817008972168), - ('diversification', 0.6479553580284119), - ('expertise', 0.6446896195411682), - ('digital', 0.6326863765716553)] -``` - -
- -```python -#to extract vector for "innovation" -w2v_model.get_vector('innovation') -``` - -Run - -``` -array([-0.45616838, -0.7799563 , 0.56367606, -0.8570078 , 0.600359 , - -0.6588043 , 0.31116748, -0.11956959, -0.47599426, 0.21840936, - -0.02268819, 0.1832016 , 0.24452794, 0.01084935, -1.4213187 , - 0.22840202, 0.46387577, 1.198386 , -0.621511 , -0.51598716, - 0.13352732, 0.04140598, -0.23470387, 0.6402956 , 0.20394802, - 0.10799981, 0.24908689, -1.0117126 , -2.3168423 , -0.0402851 , - 1.6886286 , 0.5357047 , 0.22932841, -0.6094084 , 0.4515793 , - -0.5900931 , 1.8684244 , -0.21056202, 0.29313338, -0.221067 , - -0.9535679 , 0.07325 , -0.15823542, 1.1477109 , 0.6716076 , - -1.0096023 , 0.10605699, 1.4148282 , 0.24576302, 0.5740349 , - 0.19984631, 0.53964925, 0.41962907, 0.41497853, -1.0322098 , - 0.01090925, 0.54345983, 0.806317 , 0.31737605, -0.7965337 , - 0.9282971 , -0.8775608 , -0.26852605, -0.06743863, 0.42815775, - -0.11774074, -0.17956367, 0.88813037, -0.46279573, -1.0841943 , - -0.06798118, 0.4493006 , 0.71962464, -0.02876493, 1.0282255 , - -1.1993176 , -0.38734904, -0.15875885, -0.81085825, -0.07678922, - -0.16753489, 0.14065655, -1.8609751 , 0.03587054, 1.2792674 , - 1.2732009 , -0.74120265, -0.98000383, 0.4521185 , -0.26387128, - 0.37045383, 0.3680011 , 0.7197629 , -0.3570571 , 0.8016917 , - 0.39243212, -0.5027844 , -1.2106236 , 0.6412354 , -0.878307 ], - dtype=float32) -``` - -

- - - -### 2.3 co_occurrence_matrix - -generate word co-occurrence matrix - -```python -import cntext as ct - -documents = ["I go to school every day by bus .", - "i go to theatre every night by bus"] - -ct.co_occurrence_matrix(documents, - window_size=2, - lang='english') -``` - -![](img/co_occurrence1.png) - - - -

- - - -### 2.4 Glove - -Build the Glove model for english corpus data. corpus file path is ``data/brown_corpus.txt`` - -```python -import cntext as ct -import os - -model = ct.Glove(cwd=os.getcwd(), lang='english') -model.create_vocab(file='data/brown_corpus.txt', min_count=5) -model.cooccurrence_matrix() -model.train_embeddings(vector_size=50, max_iter=25) -model.save() -``` - -Run - -``` -Step 1/4: ...Create vocabulary for Glove. -Step 2/4: ...Create cooccurrence matrix. -Step 3/4: ...Train glove embeddings. - Note, this part takes a long time to run -Step 3/4: ... Finish! Use 175.98 s -``` - -The generate生成的词嵌入模型文件位于output/Glove内 - -

- - - -## 3. similarity - -Four text similarity functions - -- **cosine_sim(text1, text2)** -- **jaccard_sim(text1, text2)** -- **minedit_sim(text1, text2)** -- **simple_sim(text1, text2)** - -Algorithm implementation reference from ``Cohen, Lauren, Christopher Malloy, and Quoc Nguyen. Lazy prices. No. w25084. National Bureau of Economic Research, 2018.`` - - - -
- -```python -import cntext as ct - - -text1 = 'Programming is fun!' -text2 = 'Programming is interesting!' - -print(ct.cosine_sim(text1, text2)) -print(ct.jaccard_sim(text1, text2)) -print(ct.minedit_sim(text1, text2)) -print(ct.simple_sim(text1, text2)) -``` - -Run - -``` -0.67 -0.50 -1.00 -0.90 -``` - -

- -## 4. Text2Mind - -Word embeddings contain human cognitive information. - -- **tm.sematic_distance(words, c_words1, c_words2)** -- **tm.sematic_projection(words, c_words1, c_words2)** - - - -### 4.1 tm.sematic_distance(words, c_words1, c_words2) - -Calculate the two semantic distance, and return the difference between the two. - -- **words** concept words, words = ['program', 'software', 'computer'] -- **c_words1** concept words1, c_words1 = ["man", "he", "him"] -- **c_words2** concept words2, c_words2 = ["woman", "she", "her"] - - - -For example, - -``` -male_concept = ['male', 'man', 'he', 'him'] - -female_concept = ['female', 'woman', 'she', 'her'] - -software_engineer_concept = ['engineer', 'programming', 'software'] - -d1 = distance(male_concept, software_engineer_concept) - -d2 = distance(female_concept, software_engineer_concept) -``` - -If d1-d2<0,it means in semantic space, between man and woman, software_engineer_concept is more closer to male_concept。 - -In other words, there is a stereotype (bias) of women for software engineers in this corpus. - -[download glove_w2v.6B.100d.txt from google Driver](https://drive.google.com/file/d/1tuQB9PDx42z67ScEQrg650aDTYPz-elJ/view?usp=sharing) - - - -```python -import cntext as ct - -#Note: this is a word2vec format model -tm = ct.Text2Mind(w2v_model_path='glove_w2v.6B.100d.txt') - -engineer = ['program', 'software', 'computer'] -mans = ["man", "he", "him"] -womans = ["woman", "she", "her"] - - -tm.sematic_distance(words=animals, - c_words1=mans, - c_words2=womans) -``` - -Run - -``` --0.38 -``` - --0.38 means in semantic space, engineer is closer to man, other than woman. - -
- -### 4.2 tm.sematic_projection(words, c_words1, c_words2) - -To explain the semantic projection of the word vector model, I use the picture from a Nature paper in 2022[@Grand2022SemanticPR]. Regarding the names of animals, human cognition information about animal size is hidden in the corpus text. By projecting the meaning of **LARGE WORDS** and **SMALL WORDS** with the vectors of different **animals**, the projection of the animal on the **size vector**(just like the red line in the bellow picture) is obtained, so the size of the animal can be compared by calculation. - -Calculate the projected length of each word vector in the concept vector.Note that the calculation result reflects the direction of concept.**Greater than 0 means semantically closer to c_words2**. - - - -> Grand, G., Blank, I.A., Pereira, F. and Fedorenko, E., 2022. Semantic projection recovers rich human knowledge of multiple object features from word embeddings. _Nature Human Behaviour_, pp.1-13. - - - - - - - -![](img/Nature_Semantic_projection_recovering_human_knowledge_of.png) - -For example, in the corpus, perhaps show that our human beings have different size memory(perception) about animals. - -```python -animals = ['mouse', 'cat', 'horse', 'pig', 'whale'] -small_words = ["small", "little", "tiny"] -large_words = ["large", "big", "huge"] - -tm.sematic_projection(words=animals, - c_words1=small_words, - c_words2=large_words) -``` - -Run - -``` -[('mouse', -1.68), - ('cat', -0.92), - ('pig', -0.46), - ('whale', -0.24), - ('horse', 0.4)] -``` - -Regarding the perception of size, humans have implied in the text that mice are smaller and horses are larger. - -

- - - -## Citation -If you use **cntext** in your research or in your project, please cite: - - -### apalike -``` -Deng X., Nan P. (2022). cntext: a Python tool for text mining (version 1.7.9). DOI: 10.5281/zenodo.7063523 URL: https://github.com/hiDaDeng/cntext -``` - -### bibtex - -``` -@misc{YourReferenceHere, -author = {Deng, Xudong and Nan, Peng}, -doi = {10.5281/zenodo.7063523}, -month = {9}, -title = {cntext: a Python tool for text mining}, -url = {https://github.com/hiDaDeng/cntext}, -year = {2022} -} -``` - -### endnote - -``` -%0 Generic -%A Deng, Xudong -%A Nan, Peng -%D 2022 -%K text mining -%K text analysi -%K social science -%K management science -%K semantic analysis -%R 10.5281/zenodo.7063523 -%T cntext: a Python tool for text mining -%U https://github.com/hiDaDeng/cntext -``` diff --git a/cntext.egg-info/SOURCES.txt b/cntext.egg-info/SOURCES.txt deleted file mode 100644 index b52944b..0000000 --- a/cntext.egg-info/SOURCES.txt +++ /dev/null @@ -1,32 +0,0 @@ -LICENSE -README.md -setup.py -cntext/__init__.py -cntext/dictionary.py -cntext/io.py -cntext/mind.py -cntext/similarity.py -cntext/stats.py -cntext.egg-info/PKG-INFO -cntext.egg-info/SOURCES.txt -cntext.egg-info/dependency_links.txt -cntext.egg-info/requires.txt -cntext.egg-info/top_level.txt -cntext/files/ADV_CONJ.pkl -cntext/files/AFINN.pkl -cntext/files/ANEW.pkl -cntext/files/ChineseEmoBank.pkl -cntext/files/ChineseFinancialFormalUnformalSentiment.pkl -cntext/files/Chinese_Digitalization.pkl -cntext/files/Chinese_FLS.pkl -cntext/files/Chinese_Loughran_McDonald_Financial_Sentiment.pkl -cntext/files/Concreteness.pkl -cntext/files/DUTIR.pkl -cntext/files/HOWNET.pkl -cntext/files/HuLiu.pkl -cntext/files/LSD2015.pkl -cntext/files/Loughran_McDonald_Financial_Sentiment.pkl -cntext/files/NRC.pkl -cntext/files/STOPWORDS.pkl -cntext/files/geninqposneg.pkl -cntext/files/sentiws.pkl \ No newline at end of file diff --git a/cntext.egg-info/dependency_links.txt b/cntext.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789..0000000 --- a/cntext.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/cntext.egg-info/requires.txt 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b/examples/.ipynb_checkpoints/01-stats-checkpoint.ipynb @@ -89,8 +89,8 @@ "output_type": "stream", "text": [ "Building prefix dict from the default dictionary ...\n", - "Loading model from cache /var/folders/sc/3mnt5tgs419_hk7s16gq61p80000gn/T/jieba.cache\n", - "Loading model cost 0.596 seconds.\n", + "Loading model from cache /var/folders/y0/4gqxky0s2t94x1c1qhlwr6100000gn/T/jieba.cache\n", + "Loading model cost 0.270 seconds.\n", "Prefix dict has been built successfully.\n" ] }, @@ -99,7 +99,7 @@ "output_type": "stream", "text": [ "Counter({'致力于': 1, '文章': 1, '处理费': 1, '订阅费': 1, '发布': 1, '优质': 1, '研究': 1, '软件': 1})\n", - "{'readability1': 23.0, 'readability2': 0.15384615384615385, 'readability3': 11.576923076923077}\n" + "{'readability1': 23.0, 'readability2': 2.0, 'readability3': 12.5}\n" ] } ], @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -144,30 +144,21 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": 3, + "metadata": { + "collapsed": true + }, "outputs": [ { - "data": { - "text/plain": [ - "['DUTIR.pkl',\n", - " 'HOWNET.pkl',\n", - " 'sentiws.pkl',\n", - " 'ChineseFinancialFormalUnformalSentiment.pkl',\n", - " 'ANEW.pkl',\n", - " 'LSD2015.pkl',\n", - " 'NRC.pkl',\n", - " 'geninqposneg.pkl',\n", - " 'HuLiu.pkl',\n", - " 'AFINN.pkl',\n", - " 'ADV_CONJ.pkl',\n", - " 'LoughranMcDonald.pkl',\n", - " 'STOPWORDS.pkl']" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" + "ename": "AttributeError", + "evalue": "module 'cntext' has no attribute 'dict_pkl_list'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcntext\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mct\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mct\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdict_pkl_list\u001b[49m()\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'cntext' has no attribute 'dict_pkl_list'" + ] } ], "source": [ @@ -181,6 +172,21 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'NRC': {'anger': ['abandoned', 'abandonment', 'abhor', 'abhorrent', 'abolish', 'abomination', 'abuse', 'accursed', 'accusation', 'accused', 'accuser', 'accusing', 'actionable', 'adder', 'adversary', 'adverse', 'adversity', 'advocacy', 'affront', 'aftermath', 'aggravated', 'aggravating', 'aggravation', 'aggression', 'aggressive', 'aggressor', 'agitated', 'agitation', 'agony', 'alcoholism', 'alienate', 'alienation', 'allegation', 'altercation', 'ambush', 'anarchism', 'anarchist', 'anarchy', 'anathema', 'anger', 'angry', 'anguish', 'animosity', 'animus', 'annihilate', 'annihilated', 'annihilation', 'annoy', 'annoyance', 'annoying', 'antagonism', 'antagonist', 'antagonistic', 'antichrist', 'antipathy', 'antisocial', 'antithesis', 'anxiety', 'argue', 'argument', 'argumentation', 'arguments', 'armament', 'armed', 'arraignment', 'arrogant', 'arson', 'assail', 'assailant', 'assassin', 'assassinate', 'assassination', 'assault', 'asshole', 'atrocious', 'atrocity', 'attack', 'attacking', 'attorney', 'avarice', 'avenger', 'averse', 'aversion', 'awful', 'backbone', 'bad', 'badger', 'badness', 'bane', 'bang', 'banger', 'banish', 'banished', 'banishment', 'bankruptcy', 'banshee', 'barb', 'barbaric', 'bark', 'barrier', 'bastion', 'battalion', 'batter', 'battery', 'battle', 'battled', 'bayonet', 'bear', 'bearish', 'beast', 'beating', 'bee', 'belittle', 'belligerent', 'bellows', 'belt', 'berserk', 'betray', 'betrayal', 'bias', 'bickering', 'bigot', 'bigoted', 'bile', 'birch', 'birthplace', 'bitch', 'bitterly', 'bitterness', 'blackmail', 'blame', 'blasphemous', 'blasphemy', 'blast', 'blatant', 'blaze', 'blemish', 'blockade', 'bloodshed', 'bloodthirsty', 'bloody', 'bogus', 'boisterous', 'bomb', 'bombard', 'bombardment', 'bothering', 'bout', 'boxing', 'brawl', 'brazen', 'brimstone', 'broil', 'broken', 'brunt', 'brutal', 'brutality', 'brute', 'buffet', 'bugaboo', 'bully', 'bummer', 'burial', 'burke', 'busted', 'butcher', 'cacophony', 'cad', 'callous', 'campaigning', 'cancer', 'cane', 'canker', 'cannon', 'carelessness', 'carnage', 'cash', 'casualty', 'catastrophe', 'caution', 'celebrity', 'censor', 'chaff', 'challenge', 'chant', 'chaos', 'chaotic', 'cheat', 'choke', 'claimant', 'clamor', 'clash', 'clashing', 'claw', 'coerce', 'coercion', 'coldness', 'collision', 'collusion', 'combat', 'combatant', 'combative', 'commotion', 'communism', 'complain', 'complaint', 'complicate', 'compress', 'compulsion', 'concealment', 'concussion', 'condemn', 'condemnation', 'condescension', 'confine', 'confined', 'confinement', 'confiscate', 'conflagration', 'conflict', 'confront', 'confusion', 'conquest', 'conspirator', 'consternation', 'constraint', 'contempt', 'contemptible', 'contemptuous', 'contentious', 'contraband', 'contradict', 'controversial', 'convict', 'coop', 'copycat', 'corrupting', 'counsellor', 'coup', 'court', 'crabby', 'cracked', 'cranky', 'crazed', 'crazy', 'crime', 'criminal', 'criminality', 'criticism', 'criticize', 'cross', 'crucifixion', 'cruel', 'cruelly', 'cruelty', 'crunch', 'crusade', 'crushed', 'crushing', 'cur', 'curse', 'cursed', 'cursing', 'cussed', 'cutthroat', 'cutting', 'dabbling', 'daemon', 'damage', 'dame', 'damn', 'damnation', 'darkness', 'dashed', 'dastardly', 'deadly', 'death', 'deceit', 'deceive', 'deceived', 'decry', 'defamatory', 'defect', 'defendant', 'defense', 'defiance', 'defiant', 'deflate', 'defraud', 'defy', 'degeneracy', 'delay', 'deleterious', 'delinquent', 'delusion', 'delusional', 'demand', 'demolish', 'demon', 'demonic', 'denounce', 'denunciation', 'deny', 'deplorable', 'deplore', 'deportation', 'depraved', 'depravity', 'depreciate', 'depreciated', 'depressed', 'deprivation', 'deranged', 'derision', 'derogation', 'derogatory', 'desecration', 'desert', 'deserted', 'deserve', 'desist', 'despair', 'despicable', 'despise', 'despotism', 'destroyed', 'destroyer', 'destroying', 'destruction', 'destructive', 'detainee', 'deterioration', 'detest', 'detonation', 'detract', 'devastate', 'devastating', 'devastation', 'devil', 'diabolical', 'diatribe', 'dictatorial', 'dictatorship', 'difficulty', 'disagree', 'disagreeing', 'disagreement', 'disallowed', 'disappoint', 'disappointed', 'disapprove', 'disapproved', 'disapproving', 'disaster', 'disastrous', 'disclaim', 'discontent', 'discord', 'discriminate', 'discrimination', 'disdain', 'disease', 'disfigured', 'disgrace', 'disgraced', 'disgraceful', 'disgruntled', 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'executioner', 'exile', 'expel', 'expletive', 'explode', 'explosive', 'expulsion', 'extermination', 'extinguish', 'failing', 'fallacious', 'falsehood', 'falsification', 'fatal', 'fear', 'fee', 'feeling', 'fenced', 'ferocious', 'ferocity', 'fervor', 'feud', 'feudalism', 'fib', 'fiend', 'fierce', 'fight', 'fighting', 'firearms', 'fits', 'flagrant', 'fleece', 'flog', 'fluctuation', 'foe', 'foray', 'forbidding', 'force', 'forcibly', 'forearm', 'forfeit', 'forsaken', 'foul', 'fraud', 'fraudulent', 'frenetic', 'frenzied', 'friction', 'frightful', 'frowning', 'frustrate', 'frustrated', 'frustration', 'fugitive', 'fuming', 'furious', 'furiously', 'furnace', 'furor', 'fury', 'fuss', 'gall', 'gallows', 'gang', 'gent', 'gibberish', 'glare', 'glaring', 'gnome', 'godless', 'gonorrhea', 'gore', 'gory', 'grab', 'grated', 'grating', 'greed', 'grievance', 'grievous', 'grim', 'grope', 'growl', 'growling', 'grudge', 'gruff', 'grumble', 'grumpy', 'guillotine', 'guilty', 'gun', 'halter', 'hamstring', 'hanging', 'harass', 'harassing', 'harbinger', 'hardened', 'harmful', 'harry', 'harshness', 'hate', 'hateful', 'hating', 'hatred', 'haughty', 'havoc', 'hell', 'hellish', 'hiss', 'hit', 'hoax', 'holocaust', 'homeless', 'homicidal', 'homicide', 'honest', 'hood', 'hoot', 'hopelessness', 'horrible', 'horrid', 'horrific', 'horror', 'hostage', 'hostile', 'hostilities', 'hostility', 'hot', 'howl', 'huff', 'humbug', 'humiliate', 'hunting', 'hurt', 'hurtful', 'hurting', 'hysterical', 'idiocy', 'idiotic', 'ill', 'illegal', 'illegality', 'illegitimate', 'illicit', 'immaturity', 'immoral', 'immorality', 'impermeable', 'implicate', 'impotence', 'imprisoned', 'imprisonment', 'inadmissible', 'inappropriate', 'inattention', 'incarceration', 'incase', 'incendiary', 'incense', 'incest', 'incite', 'incompatible', 'incompetent', 'incongruous', 'inconsiderate', 'inconvenient', 'incredulous', 'incurable', 'indecency', 'indenture', 'indict', 'indifference', 'indignant', 'indignation', 'indoctrination', 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'witchcraft', 'wop', 'words', 'worthless', 'wound', 'wrangling', 'wrath', 'wreak', 'wreck', 'wrecked', 'wretch', 'wring', 'wrongdoing', 'wrongful', 'wrongly', 'yell', 'yelp', 'youth'], 'anticipation': ['abundance', 'accelerate', 'accolade', 'accompaniment', 'achievement', 'acquiring', 'addresses', 'adore', 'adrift', 'advance', 'advent', 'adventure', 'advocacy', 'airport', 'alertness', 'alerts', 'alive', 'allure', 'aloha', 'ambition', 'amour', 'analyst', 'angel', 'angling', 'announcement', 'anticipation', 'anticipatory', 'antidote', 'anxiety', 'anxious', 'appeal', 'applicant', 'apprehensive', 'approaching', 'arbitration', 'archaeology', 'ardent', 'arouse', 'arrival', 'arrive', 'art', 'aspiration', 'aspire', 'aspiring', 'astrologer', 'astronomer', 'atone', 'attainable', 'attempt', 'attendance', 'auction', 'audience', 'auspicious', 'await', 'award', 'ballot', 'balm', 'banger', 'banquet', 'basketball', 'beaming', 'begun', 'betrothed', 'beware', 'biennial', 'birth', 'birthday', 'bless', 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'morality', 'morals', 'mortgagee', 'mother', 'motherhood', 'mule', 'musical', 'nation', 'navigator', 'negotiate', 'neighbor', 'nest', 'nestle', 'neutral', 'neutrality', 'nobility', 'noble', 'nobleman', 'notable', 'notary', 'nun', 'nurse', 'nursery', 'nurture', 'oasis', 'oath', 'obedience', 'obey', 'objective', 'oblige', 'obliging', 'obstetrician', 'obvious', 'occupant', 'offering', 'officer', 'official', 'omniscient', 'opera', 'operation', 'optimism', 'optimist', 'oracle', 'orchestra', 'ordained', 'ordinance', 'ordination', 'organization', 'orthodoxy', 'owing', 'pact', 'paragon', 'parietal', 'parish', 'parliament', 'partake', 'partnership', 'passion', 'passionate', 'pastor', 'patience', 'patriarchal', 'patrol', 'patron', 'patronage', 'pavement', 'pawn', 'pay', 'peace', 'peaceful', 'pedigree', 'perceive', 'perennial', 'perfect', 'perfection', 'periodicity', 'perpetuity', 'personal', 'persuade', 'pertinent', 'phalanx', 'philanthropic', 'philanthropist', 'philosopher', 'physician', 'physicist', 'picnic', 'pill', 'pilot', 'pious', 'pith', 'pivot', 'planning', 'playful', 'playground', 'pleasant', 'pledge', 'police', 'policeman', 'policy', 'poll', 'pontiff', 'possess', 'powerful', 'practiced', 'praise', 'praiseworthy', 'pray', 'precedence', 'predominant', 'prefer', 'prepared', 'preponderance', 'present', 'preservative', 'president', 'prestige', 'presumption', 'pretty', 'prevalent', 'priest', 'priesthood', 'primer', 'princely', 'principal', 'privileged', 'privy', 'probity', 'proctor', 'professional', 'professor', 'professorship', 'proficiency', 'proficient', 'progression', 'promise', 'proof', 'prophet', 'prophylactic', 'protected', 'protecting', 'protector', 'proud', 'proven', 'provide', 'providing', 'proviso', 'proxy', 'prudent', 'pry', 'punctual', 'puppy', 'purely', 'purification', 'purify', 'purr', 'putative', 'quaint', 'qualified', 'radar', 'radiance', 'ranger', 'rapt', 'ratify', 'rational', 'rave', 'readiness', 'real', 'reassurance', 'reassure', 'recline', 'recommend', 'reconciliation', 'reconsideration', 'recruits', 'referee', 'regal', 'regent', 'registry', 'regularity', 'reimbursement', 'reinforcement', 'reinforcements', 'rejoice', 'related', 'relative', 'relevant', 'reliability', 'reliable', 'reliance', 'religion', 'remains', 'remarkable', 'remedy', 'reparation', 'repay', 'reporter', 'reputable', 'rescue', 'resources', 'respect', 'respectable', 'respectful', 'respects', 'respite', 'responsible', 'responsive', 'restorative', 'retain', 'retirement', 'reunion', 'revere', 'reverence', 'reverie', 'revival', 'reward', 'roadster', 'rod', 'romance', 'romantic', 'rooted', 'rota', 'routine', 'rule', 'sadness', 'safe', 'safeguard', 'safekeeping', 'sage', 'saint', 'saintly', 'salary', 'salutary', 'salvation', 'sanctification', 'sanctify', 'sanctuary', 'sappy', 'save', 'savor', 'sceptical', 'school', 'scientific', 'scientist', 'scoundrel', 'scripture', 'seal', 'seals', 'secrecy', 'secret', 'securities', 'senate', 'seniority', 'sensual', 'sentinel', 'sentry', 'serene', 'serenity', 'series', 'sermon', 'servant', 'serve', 'sex', 'share', 'shelter', 'shepherd', 'sheriff', 'shopkeeper', 'shopping', 'shoulder', 'show', 'sib', 'signature', 'simplify', 'sincere', 'sing', 'sir', 'sisterhood', 'skillful', 'smile', 'smith', 'sneaking', 'sobriety', 'solidarity', 'solidity', 'soothing', 'soundness', 'sovereign', 'spa', 'spaniel', 'specialist', 'specialize', 'spokesman', 'sponsor', 'spotless', 'spouse', 'stable', 'stamina', 'star', 'stark', 'statement', 'statistical', 'steadfast', 'steady', 'sterling', 'steward', 'straightforward', 'strategist', 'strength', 'strengthening', 'structural', 'structure', 'substantiate', 'succeed', 'succeeding', 'successful', 'suggest', 'sun', 'sundial', 'superman', 'superstar', 'supplication', 'supporter', 'supporting', 'supremacy', 'surety', 'surrogate', 'swear', 'sweet', 'sweetheart', 'symmetry', 'sympathetic', 'synchronize', 'synergistic', 'synod', 'synonymous', 'system', 'tactics', 'tandem', 'tantamount', 'taught', 'teach', 'teacher', 'team', 'temperate', 'tender', 'testament', 'testimony', 'theocratic', 'theological', 'theorem', 'theory', 'therapeutic', 'thermometer', 'thoughtful', 'thrift', 'throne', 'tickle', 'title', 'top', 'toughness', 'trade', 'trainer', 'tranquility', 'transaction', 'transcendence', 'transcript', 'translation', 'treasure', 'treasurer', 'treat', 'tree', 'tribe', 'tribunal', 'tribune', 'triumphant', 'trophy', 'truce', 'truck', 'true', 'truss', 'trust', 'trustee', 'truth', 'truthful', 'truthfulness', 'tutelage', 'umpire', 'unaccountable', 'unbroken', 'understanding', 'underwrite', 'undying', 'unequivocal', 'unification', 'united', 'unity', 'unquestionable', 'unquestionably', 'unquestioned', 'unreliable', 'unsurpassed', 'unwavering', 'uplift', 'upright', 'usher', 'usual', 'utopian', 'valor', 'veal', 'venerable', 'veracity', 'verification', 'verified', 'verily', 'veteran', 'vicar', 'victory', 'vigilance', 'vigilant', 'vigorous', 'villager', 'vindication', 'virgin', 'virtue', 'virtuous', 'visionary', 'vitality', 'volunteer', 'volunteers', 'vote', 'votive', 'vouch', 'voucher', 'vow', 'warden', 'warn', 'warranty', 'watchdog', 'watchful', 'watchman', 'wealth', 'wear', 'weigh', 'weight', 'white', 'wholesome', 'winning', 'wisdom', 'witness', 'wonderful', 'word', 'worship', 'worthy', 'wot', 'yearning', 'zeal', 'zealous', 'zest']}, 'Refer': 'Crowdsourcing a Word-Emotion Association Lexicon, Saif Mohammad and Peter Turney, Computational Intelligence, 29 (3), 436-465, 2013.', 'Desc': 'The NRC Emotion Lexicon is a list of English words and their associations with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and positive). The annotations were manually done by crowdsourcing.'}\n" + ] + } + ], "source": [ "print(ct.load_pkl_dict('NRC.pkl'))" ] @@ -194,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -203,12 +209,12 @@ "text": [ "Help on function sentiment in module cntext.stats:\n", "\n", - "sentiment(text, diction, language='chinese')\n", - " calculate the occurrences of each emotional category words in text;\n", + "sentiment(text, diction, lang='chinese')\n", + " calculate the occurrences of each sentiment category words in text;\n", " the complex influence of intensity adverbs and negative words on emotion is not considered,\n", " :param text: text sring\n", - " :param diction: emotion dictionary;\n", - " :param language: 语言类型,\"chinese\"或\"english\",默认\"chinese\"\n", + " :param diction: sentiment dictionary;\n", + " :param lang: \"chinese\" or \"english\"; default lang=\"chinese\"\n", " \n", " diction = {'category1': 'category1 emotion word list',\n", " 'category2': 'category2 emotion word list',\n", @@ -226,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": { "collapsed": true }, @@ -234,19 +240,19 @@ { "data": { "text/plain": [ - "{'哀_num': 0,\n", + "{'乐_num': 2,\n", " '好_num': 0,\n", - " '惊_num': 0,\n", - " '惧_num': 0,\n", - " '乐_num': 2,\n", " '怒_num': 0,\n", + " '哀_num': 0,\n", + " '惧_num': 0,\n", " '恶_num': 0,\n", + " '惊_num': 0,\n", " 'stopword_num': 8,\n", " 'word_num': 14,\n", " 'sentence_num': 1}" ] }, - "execution_count": 4, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -261,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -277,7 +283,7 @@ " 'sentence_num': 1}" ] }, - "execution_count": 5, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -309,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -330,7 +336,7 @@ " 'sentence_num': 1}" ] }, - "execution_count": 13, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -381,14 +387,99 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "'1.8.1'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ct.__version__" + ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Counter({'看待': 1,\n", + " '网文': 1,\n", + " '作者': 1,\n", + " '黑客': 1,\n", + " '大佬': 1,\n", + " '盗号': 1,\n", + " '改文': 1,\n", + " '万分': 1,\n", + " '惭愧': 1,\n", + " '停': 1})" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import cntext as ct\n", + "\n", + "text = '如何看待一网文作者被黑客大佬盗号改文,因万分惭愧而停更。'\n", + "\n", + "ct.term_freq(text, lang='chinese')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Referer': 'Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46, 904–911',\n", + " 'Desc': '语言具体性词典, 具体性计算应用案例可参考Packard, Grant, and Jonah Berger. \"How concrete language shapes customer satisfaction.\" *Journal of Consumer Research* 47, no. 5 (2021): 787-806.',\n", + " 'Concreteness': word valence\n", + " 0 roadsweeper 4.85\n", + " 1 traindriver 4.54\n", + " 2 tush 4.45\n", + " 3 hairdress 3.93\n", + " 4 pharmaceutics 3.77\n", + " ... ... ...\n", + " 39949 unenvied 1.21\n", + " 39950 agnostically 1.20\n", + " 39951 conceptualistic 1.18\n", + " 39952 conventionalism 1.18\n", + " 39953 essentialness 1.04\n", + " \n", + " [39954 rows x 2 columns]}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import cntext as ct\n", + "\n", + "# load the concreteness.pkl dictionary file\n", + "concreteness_data = ct.load_pkl_dict('concreteness.pkl')\n", + "concreteness_data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -455,31 +546,28 @@ "4 pharmaceutics 3.77" ] }, - "execution_count": 1, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import cntext as ct\n", - "\n", - "# load the concreteness.pkl dictionary file\n", - "concreteness_df = ct.load_pkl_dict('concreteness.pkl')\n", + "concreteness_df = concreteness_data['Concreteness']\n", "concreteness_df.head()" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "1.856" + "{'valence': 9.28, 'word_num': 5}" ] }, - "execution_count": 2, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -495,19 +583,19 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Concreteness Score: 1.86 | Example-0: I'll go look for that\n", - "Concreteness Score: 1.86 | Example-1: I'll go search for that\n", - "Concreteness Score: 2.21 | Example-2: I'll go search for that top\n", - "Concreteness Score: 2.04 | Example-3: I'll go search for that t-shirt\n", - "Concreteness Score: 2.37 | Example-4: I'll go look for that t-shirt in grey\n", - "Concreteness Score: 2.37 | Example-5: I'll go search for that t-shirt in grey\n" + "Concreteness Score: 9.28 | Example-0: I'll go look for that\n", + "Concreteness Score: 9.32 | Example-1: I'll go search for that\n", + "Concreteness Score: 13.25 | Example-2: I'll go search for that top\n", + "Concreteness Score: 14.26 | Example-3: I'll go search for that t-shirt\n", + "Concreteness Score: 21.33 | Example-4: I'll go look for that t-shirt in grey\n", + "Concreteness Score: 21.37 | Example-5: I'll go search for that t-shirt in grey\n" ] } ], @@ -526,7 +614,7 @@ " \n", " template = \"Concreteness Score: {score:.2f} | Example-{idx}: {exmaple}\"\n", " \n", - " print(template.format(score=score, \n", + " print(template.format(score=score['valence'], \n", " idx=idx, \n", " exmaple=reply))" ] @@ -534,7 +622,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -548,7 +636,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.11.5" }, "toc": { "base_numbering": 1, @@ -562,6 +650,11 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": false + }, + "vscode": { + "interpreter": { + "hash": "39efd68f93e60613a22adb0a24f8974eb44aba5452699b12e52bf7b8f47c9669" + } } }, "nbformat": 4, diff --git a/examples/00-cntext.ipynb b/examples/00-cntext.ipynb index d27d582..2efe915 100644 --- a/examples/00-cntext.ipynb +++ b/examples/00-cntext.ipynb @@ -2,8 +2,10 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": 1, + "metadata": { + "collapsed": true + }, "outputs": [ { "name": "stdout", @@ -15,13 +17,23 @@ " cntext\n", "\n", "PACKAGE CONTENTS\n", - " bias\n", - " dictionary\n", + " align\n", + " align2\n", + " io\n", + " mind\n", + " model\n", + " psymatrix\n", " similarity\n", " stats\n", "\n", + "DATA\n", + " CONCEPTS_PAIRS = {'主观客观': {'neg': ['客观', '科学', '论证', '论据', '事实', '数据',...\n", + "\n", + "VERSION\n", + " 2.0.0\n", + "\n", "FILE\n", - " /Library/Frameworks/anaconda3/lib/python3.9/site-packages/cntext/__init__.py\n", + " /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/cntext/__init__.py\n", "\n", "\n" ] @@ -35,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -74,7 +86,7 @@ " :return:\n", "\n", "FILE\n", - " /Library/Frameworks/anaconda3/lib/python3.9/site-packages/cntext/similarity.py\n", + " /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/cntext/similarity.py\n", "\n", "\n" ] @@ -95,7 +107,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.12 ('base')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -109,7 +121,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.11.5" }, "toc": { "base_numbering": 1, diff --git a/examples/01-stats.ipynb b/examples/01-stats.ipynb index a72333e..d3a2d14 100644 --- a/examples/01-stats.ipynb +++ b/examples/01-stats.ipynb @@ -81,15 +81,25 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Building prefix dict from the default dictionary ...\n", + "Loading model from cache /var/folders/y0/4gqxky0s2t94x1c1qhlwr6100000gn/T/jieba.cache\n", + "Loading model cost 0.270 seconds.\n", + "Prefix dict has been built successfully.\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ "Counter({'致力于': 1, '文章': 1, '处理费': 1, '订阅费': 1, '发布': 1, '优质': 1, '研究': 1, '软件': 1})\n", - "{'readability1': 23.0, 'readability2': 0.15384615384615385, 'readability3': 11.576923076923077}\n" + "{'readability1': 23.0, 'readability2': 2.0, 'readability3': 12.5}\n" ] } ], @@ -103,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -134,34 +144,21 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 3, + "metadata": { + "collapsed": true + }, "outputs": [ { - "data": { - "text/plain": [ - "['ADV_CONJ.pkl',\n", - " 'AFINN.pkl',\n", - " 'ANEW.pkl',\n", - " 'ChineseEmoBank.pkl',\n", - " 'ChineseFinancialFormalUnformalSentiment.pkl',\n", - " 'Chinese_Digitalization.pkl',\n", - " 'Chinese_Loughran_McDonald_Financial_Sentiment.pkl',\n", - " 'Concreteness.pkl',\n", - " 'DUTIR.pkl',\n", - " 'geninqposneg.pkl',\n", - " 'HOWNET.pkl',\n", - " 'HuLiu.pkl',\n", - " 'Loughran_McDonald_Financial_Sentiment.pkl',\n", - " 'LSD2015.pkl',\n", - " 'NRC.pkl',\n", - " 'sentiws.pkl',\n", - " 'STOPWORDS.pkl']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" + "ename": "AttributeError", + "evalue": "module 'cntext' has no attribute 'dict_pkl_list'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mcntext\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mct\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mct\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdict_pkl_list\u001b[49m()\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'cntext' has no attribute 'dict_pkl_list'" + ] } ], "source": [ @@ -625,7 +622,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.12 ('base')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -639,7 +636,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.11.5" }, "toc": { "base_numbering": 1, diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..34360b7 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,37 @@ +[tool.poetry] +name = "cntext" +version = "1.8.8" +description = "Chinese text analysis library, which can perform word frequency statistics, dictionary expansion, sentiment analysis, similarity, readability, co-occurrence analysis, social computing (attitude, prejudice, culture) on texts" +authors = ["DaDeng"] +license = "MIT" +readme = "README.md" + +[tool.poetry.dependencies] +python = "3.11.5" + + +[dependencies] +python = "^3.5" +jieba = "^0.4.1" +numpy = "^1.21.0" +mittens = "^0.2" +scikit-learn = "^1.3.0" +matplotlib = "^3.6.0" +pyecharts = "2.0.0" +gensim = "^4.2.0" +nltk = "^3.8" +pandas = "^1.5.0" +chardet = "^5.0.0" +h5py = "^3.9.0" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + + + +[options] +package_data = "files/*.pkl" +include_package_data = true +zip_safe = false \ No newline at end of file

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