Twitter-Sentiment-Analysis

Sentiment Analysis for Racist/Sexist tweets and labelling test dataset after modeling and prediction

Context The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.

Formally, given a training sample of tweets and labels, where label '1' denotes the tweet is racist/sexist and label '0' denotes the tweet is not racist/sexist, your objective is to predict the labels on the test dataset.

Content Full tweet texts are provided with their labels for training data. Mentioned users' username is replaced with @user.

Used dataset of Twitter Sentiment Analysis imported from Kaggle with 3 columns intrain dataset id, tweet, label Test dataset has 2 columns id and tweet https://www.kaggle.com/datasets/arkhoshghalb/twitter-sentiment-analysis-hatred-speech

Steps:

1. convert tweets in lowercase
2. with nltk download english stopwords and filter stopwords from tweets
3. with re.sub remove alphanumeric and punctuations
4. now we cau use tokenizer, word2vector, tf-idf for converting words into numeric value for modeling. I am using tokenizer(word frequency in text/document)
5. fit tokenizer for train tweets
6. convert text to sequences
7. pad sequences for uniformity(fixed features/attributes) and build model using embedding, LSTM, Dense, layers
8. compile model with optimizer(Adam)
9. predict test data after modeling