Intro_NLP.ipynb

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  "metadata": {
    "colab": {
      "name": "Untitled1.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kumartr/NLP/blob/master/Intro_NLP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "lRRCPaC96WLR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "d07b9549-e2f1-45f3-ecab-8fdf4e658acd"
      },
      "cell_type": "code",
      "source": [
        "!pip install -U nltk"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting nltk\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/6f/ed/9c755d357d33bc1931e157f537721efb5b88d2c583fe593cc09603076cc3/nltk-3.4.zip (1.4MB)\n",
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            "\u001b[?25hRequirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from nltk) (1.11.0)\n",
            "Collecting singledispatch (from nltk)\n",
            "  Downloading https://files.pythonhosted.org/packages/c5/10/369f50bcd4621b263927b0a1519987a04383d4a98fb10438042ad410cf88/singledispatch-3.4.0.3-py2.py3-none-any.whl\n",
            "Building wheels for collected packages: nltk\n",
            "  Running setup.py bdist_wheel for nltk ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/4b/c8/24/b2343664bcceb7147efeb21c0b23703a05b23fcfeaceaa2a1e\n",
            "Successfully built nltk\n",
            "Installing collected packages: singledispatch, nltk\n",
            "  Found existing installation: nltk 3.2.5\n",
            "    Uninstalling nltk-3.2.5:\n",
            "      Successfully uninstalled nltk-3.2.5\n",
            "Successfully installed nltk-3.4 singledispatch-3.4.0.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Y0Ih5-Z_6ZlN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Sample code to remove noisy words from a text\n",
        "\n",
        "noise_list = [\"is\", \"a\", \"this\", \"...\"] \n",
        "def _remove_noise(input_text):\n",
        "    words = input_text.split() \n",
        "    noise_free_words = [word for word in words if word not in noise_list] \n",
        "    noise_free_text = \" \".join(noise_free_words) \n",
        "    return noise_free_text\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vDAfhOmN7GP5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "84e187c3-14c6-4f8d-e13c-4416349ff7de"
      },
      "cell_type": "code",
      "source": [
        "_remove_noise(\"this is a sample text\")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'sample text'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "tU1-Pman7aWs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "096819b2-8e82-4310-e64f-e793bbc76aed"
      },
      "cell_type": "code",
      "source": [
        "# Sample code to remove a regex pattern \n",
        "import re \n",
        "\n",
        "def _remove_regex(input_text, regex_pattern):\n",
        "    urls = re.finditer(regex_pattern, input_text) \n",
        "    for i in urls: \n",
        "        input_text = re.sub(i.group().strip(), '', input_text)\n",
        "    return input_text\n",
        "\n",
        "regex_pattern = \"#[\\w]*\"  \n",
        "\n",
        "_remove_regex(\"remove this #hashtag from analytics vidhya\", regex_pattern)\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'remove this  from analytics vidhya'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "L5SKtsPm8WM8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "8641d789-6b31-4c9b-cb86-5a86844dfc86"
      },
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('wordnet')\n",
        "\n",
        "from nltk.stem.wordnet import WordNetLemmatizer \n",
        "lem = WordNetLemmatizer()\n",
        "\n",
        "from nltk.stem.porter import PorterStemmer \n",
        "stem = PorterStemmer()\n",
        "\n",
        "word = \"multiplying\" \n",
        "lem.lemmatize(word, \"v\")\n",
        "stem.stem(word)\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'multipli'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "8m08_jGg9UcZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "7f9ea32d-f81c-4394-e185-d54279794179"
      },
      "cell_type": "code",
      "source": [
        "lem.lemmatize(word, \"v\")"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'multiply'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "qBMEm-eE9VMC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "f998ba07-b69c-482f-90a2-3790cf7d7747"
      },
      "cell_type": "code",
      "source": [
        "lookup_dict = {'rt':'Retweet', 'dm':'direct message', \"awsm\" : \"awesome\", \"luv\" :\"love\"}\n",
        "def _lookup_words(input_text):\n",
        "    words = input_text.split() \n",
        "    new_words = [] \n",
        "    for word in words:\n",
        "        if word.lower() in lookup_dict:\n",
        "            word = lookup_dict[word.lower()]\n",
        "        new_words.append(word) \n",
        "    new_text = \" \".join(new_words) \n",
        "    return new_text\n",
        "\n",
        "_lookup_words(\"RT this is a retweeted tweet by Shivam Bansal\")"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Retweet this is a retweeted tweet by Shivam Bansal'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "lcUobEytYj7_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 149
        },
        "outputId": "c2f54372-acd2-4b3d-e8d4-b4d5d9b3b328"
      },
      "cell_type": "code",
      "source": [
        "from nltk import word_tokenize, pos_tag\n",
        "nltk.download('punkt')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "text = \"I am learning Natural Language Processing on Analytics Vidhya\"\n",
        "tokens = word_tokenize(text)\n",
        "print(pos_tag(tokens))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('I', 'PRP'), ('am', 'VBP'), ('learning', 'VBG'), ('Natural', 'NNP'), ('Language', 'NNP'), ('Processing', 'NNP'), ('on', 'IN'), ('Analytics', 'NNP'), ('Vidhya', 'NNP')]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "ucfjvc2WbIsY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "outputId": "e517831a-7e67-4caf-affb-b7265e2af1e4"
      },
      "cell_type": "code",
      "source": [
        "!pip install --upgrade gensim\n",
        "!pip install --upgrade corpora\n",
        "!pip install --upgrade corpus\n",
        "\n",
        "\n",
        "doc1 = \"Sugar is bad to consume. My sister likes to have sugar, but not my father.\" \n",
        "doc2 = \"My father spends a lot of time driving my sister around to dance practice.\"\n",
        "doc3 = \"Doctors suggest that driving may cause increased stress and blood pressure.\"\n",
        "doc_complete = [doc1, doc2, doc3]\n",
        "doc_clean = [doc.split() for doc in doc_complete]\n",
        "\n",
        "import gensim\n",
        "import gensim.corpora\n",
        "\n",
        "\n",
        "# Creating the term dictionary of our corpus, where every unique term is assigned an index.  \n",
        "dictionary = gensim.corpora.Dictionary(doc_clean)\n",
        "\n",
        "# Converting list of documents (corpus) into Document Term Matrix using dictionary prepared above. \n",
        "doc_term_matrix = [dictionary.doc2bow(doc) for doc in doc_clean]\n",
        "\n",
        "# Creating the object for LDA model using gensim library\n",
        "Lda = gensim.models.ldamodel.LdaModel\n",
        "\n",
        "# Running and Training LDA model on the document term matrix\n",
        "ldamodel = Lda(doc_term_matrix, num_topics=3, id2word = dictionary, passes=50)\n",
        "\n",
        "# Results \n",
        "print(ldamodel.print_topics())"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: gensim in /usr/local/lib/python3.6/dist-packages (3.6.0)\n",
            "Requirement already satisfied, skipping upgrade: scipy>=0.18.1 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.1.0)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.5.0 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.11.0)\n",
            "Requirement already satisfied, skipping upgrade: smart-open>=1.2.1 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.7.1)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.14.6)\n",
            "Requirement already satisfied, skipping upgrade: bz2file in /usr/local/lib/python3.6/dist-packages (from smart-open>=1.2.1->gensim) (0.98)\n",
            "Requirement already satisfied, skipping upgrade: boto3 in /usr/local/lib/python3.6/dist-packages (from smart-open>=1.2.1->gensim) (1.9.57)\n",
            "Requirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.6/dist-packages (from smart-open>=1.2.1->gensim) (2.18.4)\n",
            "Requirement already satisfied, skipping upgrade: boto>=2.32 in /usr/local/lib/python3.6/dist-packages (from smart-open>=1.2.1->gensim) (2.49.0)\n",
            "Requirement already satisfied, skipping upgrade: jmespath<1.0.0,>=0.7.1 in /usr/local/lib/python3.6/dist-packages (from boto3->smart-open>=1.2.1->gensim) (0.9.3)\n",
            "Requirement already satisfied, skipping upgrade: s3transfer<0.2.0,>=0.1.10 in /usr/local/lib/python3.6/dist-packages (from boto3->smart-open>=1.2.1->gensim) (0.1.13)\n",
            "Requirement already satisfied, skipping upgrade: botocore<1.13.0,>=1.12.57 in /usr/local/lib/python3.6/dist-packages (from boto3->smart-open>=1.2.1->gensim) (1.12.57)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim) (2018.10.15)\n",
            "Requirement already satisfied, skipping upgrade: urllib3<1.23,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim) (1.22)\n",
            "Requirement already satisfied, skipping upgrade: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim) (2.6)\n",
            "Requirement already satisfied, skipping upgrade: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: docutils>=0.10 in /usr/local/lib/python3.6/dist-packages (from botocore<1.13.0,>=1.12.57->boto3->smart-open>=1.2.1->gensim) (0.14)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil<3.0.0,>=2.1; python_version >= \"2.7\" in /usr/local/lib/python3.6/dist-packages (from botocore<1.13.0,>=1.12.57->boto3->smart-open>=1.2.1->gensim) (2.5.3)\n",
            "Requirement already up-to-date: corpora in /usr/local/lib/python3.6/dist-packages (1.0)\n",
            "Requirement already up-to-date: corpus in /usr/local/lib/python3.6/dist-packages (0.4.2)\n",
            "[(0, '0.089*\"to\" + 0.051*\"My\" + 0.051*\"sister\" + 0.051*\"my\" + 0.051*\"likes\" + 0.051*\"sugar,\" + 0.051*\"not\" + 0.051*\"Sugar\" + 0.051*\"but\" + 0.051*\"have\"'), (1, '0.053*\"driving\" + 0.053*\"my\" + 0.053*\"My\" + 0.053*\"sister\" + 0.053*\"father\" + 0.053*\"around\" + 0.053*\"a\" + 0.053*\"time\" + 0.053*\"spends\" + 0.053*\"lot\"'), (2, '0.060*\"driving\" + 0.060*\"increased\" + 0.060*\"and\" + 0.060*\"blood\" + 0.060*\"cause\" + 0.060*\"may\" + 0.060*\"suggest\" + 0.060*\"stress\" + 0.060*\"Doctors\" + 0.060*\"pressure.\"')]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9ZapaXWYim__",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MIGV67HiioFe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}