Natural_Language_Processing_in_Python_Simplified_Text_Processing_with TextBlob.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Natural Language Processing in Python: Simplified Text Processing with TextBlob.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMduYvXCUVnPJ8HJV6lWe9e",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/siglimumuni/Published-Articles/blob/main/Natural_Language_Processing_in_Python_Simplified_Text_Processing_with%C2%A0TextBlob.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kjfyt5P_jd-s"
      },
      "outputs": [],
      "source": [
        "#import relevant libraries\n",
        "from textblob import TextBlob"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#sentence tokenization\n",
        "text = TextBlob(\"I must not fear. Fear is the mind-killer. \\\n",
        "                 Fear is the little-death that brings total obliteration. \\\n",
        "                 I will face my fear. I will permit it to pass over me and through me. \\\n",
        "                 And when it has gone past I will turn the inner eye to see its path. \\\n",
        "                 Where the fear has gone there will be nothing. Only I will remain.\")\n",
        "text.sentences"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pJdNAHUXmbM4",
        "outputId": "c45f1ffa-62f1-4014-cfa0-c20ed4bc7c18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Sentence(\"I must not fear.\"),\n",
              " Sentence(\"Fear is the mind-killer.\"),\n",
              " Sentence(\"Fear is the little-death that brings total obliteration.\"),\n",
              " Sentence(\"I will face my fear.\"),\n",
              " Sentence(\"I will permit it to pass over me and through me.\"),\n",
              " Sentence(\"And when it has gone past I will turn the inner eye to see its path.\"),\n",
              " Sentence(\"Where the fear has gone there will be nothing.\"),\n",
              " Sentence(\"Only I will remain.\")]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#word tokenization\n",
        "text = TextBlob(\"It is only with the heart that one can see rightly; what is essential is invisible to the eye.\")\n",
        "\n",
        "text.words"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qXYebMId4LM8",
        "outputId": "1b44464b-a69e-403e-da1a-6d803526ebbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "WordList(['It', 'is', 'only', 'with', 'the', 'heart', 'that', 'one', 'can', 'see', 'rightly', 'what', 'is', 'essential', 'is', 'invisible', 'to', 'the', 'eye'])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#POS tagging\n",
        "text = TextBlob(\"What happens to a dream deferred?\\\n",
        "                Does it dry up\\\n",
        "                like a raisin in the sun?\")\n",
        "\n",
        "text.tags"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dlUL3m4Hfm9j",
        "outputId": "4c1d6352-4f9e-4fc9-97e1-c4029b1a9b88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('What', 'WP'),\n",
              " ('happens', 'VBZ'),\n",
              " ('to', 'TO'),\n",
              " ('a', 'DT'),\n",
              " ('dream', 'NN'),\n",
              " ('deferred', 'VBN'),\n",
              " ('Does', 'VBZ'),\n",
              " ('it', 'PRP'),\n",
              " ('dry', 'VB'),\n",
              " ('up', 'RP'),\n",
              " ('like', 'IN'),\n",
              " ('a', 'DT'),\n",
              " ('raisin', 'NN'),\n",
              " ('in', 'IN'),\n",
              " ('the', 'DT'),\n",
              " ('sun', 'NN')]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#lemmatization\n",
        "from textblob import Word\n",
        "\n",
        "wordlist = ['went','teaches','flew','eating','learned']\n",
        "for word in wordlist:\n",
        "  w = Word(word)\n",
        "  print(w.lemmatize('v'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xmBkcZ7XZZI7",
        "outputId": "aaafef2e-816a-4c68-8c0a-eb729573990b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "go\n",
            "teach\n",
            "fly\n",
            "eat\n",
            "learn\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#word frequency\n",
        "text = TextBlob(\"It was the best of times, it was the worst of times, it was the age of wisdom,\\\n",
        "        it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity,\\\n",
        "        it was the season of Light, it was the season of Darkness, it was the spring of hope,\\\n",
        "        it was the winter of despair, we had everything before us, we had nothing before us,\\\n",
        "        we were all going direct to Heaven, we were all going direct the other way — in short,\\\n",
        "        the period was so far like the present period, that some of its noisiest authorities insisted on its being received,\\\n",
        "        for good or for evil, in the superlative degree of comparison only.\")\n",
        "\n",
        "text.word_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mEqNPvoPaTWD",
        "outputId": "62542ff8-1345-44ec-83d4-89e25f2b83af"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "defaultdict(int,\n",
              "            {'it': 10,\n",
              "             'was': 11,\n",
              "             'the': 14,\n",
              "             'best': 1,\n",
              "             'of': 12,\n",
              "             'times': 2,\n",
              "             'worst': 1,\n",
              "             'age': 2,\n",
              "             'wisdom': 1,\n",
              "             'foolishness': 1,\n",
              "             'epoch': 2,\n",
              "             'belief': 1,\n",
              "             'incredulity': 1,\n",
              "             'season': 2,\n",
              "             'light': 1,\n",
              "             'darkness': 1,\n",
              "             'spring': 1,\n",
              "             'hope': 1,\n",
              "             'winter': 1,\n",
              "             'despair': 1,\n",
              "             'we': 4,\n",
              "             'had': 2,\n",
              "             'everything': 1,\n",
              "             'before': 2,\n",
              "             'us': 2,\n",
              "             'nothing': 1,\n",
              "             'were': 2,\n",
              "             'all': 2,\n",
              "             'going': 2,\n",
              "             'direct': 2,\n",
              "             'to': 1,\n",
              "             'heaven': 1,\n",
              "             'other': 1,\n",
              "             'way': 1,\n",
              "             '—': 1,\n",
              "             'in': 2,\n",
              "             'short': 1,\n",
              "             'period': 2,\n",
              "             'so': 1,\n",
              "             'far': 1,\n",
              "             'like': 1,\n",
              "             'present': 1,\n",
              "             'that': 1,\n",
              "             'some': 1,\n",
              "             'its': 2,\n",
              "             'noisiest': 1,\n",
              "             'authorities': 1,\n",
              "             'insisted': 1,\n",
              "             'on': 1,\n",
              "             'being': 1,\n",
              "             'received': 1,\n",
              "             'for': 2,\n",
              "             'good': 1,\n",
              "             'or': 1,\n",
              "             'evil': 1,\n",
              "             'superlative': 1,\n",
              "             'degree': 1,\n",
              "             'comparison': 1,\n",
              "             'only': 1})"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "#convert to dataframe\n",
        "word_counts = pd.DataFrame.from_dict(text.word_counts,orient='index',columns=['Count'])\n",
        "\n",
        "#plot 20 most common words\n",
        "word_counts['Count'].sort_values(ascending=False).head(20).plot(kind='bar',title='Top 20 Most Frequent Words')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "xpzHu77bbUKQ",
        "outputId": "29c71102-ec4e-492f-a73a-5a783cdd4b18"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f552798d190>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from textblob import Word\n",
        "\n",
        "#word definition\n",
        "word = Word(\"innuendo\")\n",
        "\n",
        "word.definitions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BIeyK5Y1R0ec",
        "outputId": "1774decc-c994-4e6d-f4b9-15636fa3d88d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['an indirect (and usually malicious) implication']"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#spell check\n",
        "text = TextBlob(\"Begining tomorow, he plans to mark every occurence in his calandar\")\n",
        "\n",
        "text.correct()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sge5YjC8Ts2f",
        "outputId": "6bc5251c-cc86-414b-8f79-19febf7c8962"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TextBlob(\"Beginning tomorrow, he plans to mark every occurrence in his calendar\")"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#spell check individual words\n",
        "from textblob import Word\n",
        "word = Word(\"peice\")\n",
        "\n",
        "word.spellcheck()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xdydoO9fWnTS",
        "outputId": "4bb5fbcc-9996-4bc5-f2fb-31d517b0dfef"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('peace', 0.6735905044510386),\n",
              " ('piece', 0.18100890207715134),\n",
              " ('price', 0.13353115727002968),\n",
              " ('pence', 0.011869436201780416)]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#sentiment analysis\n",
        "text = TextBlob(\"Isn’t it nice to think that tomorrow is a new day with no mistakes in it yet?\")\n",
        "\n",
        "text.sentiment"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g5SO858RZNLF",
        "outputId": "c4eaa2dd-57ee-4cda-f5f9-cbb1686e614b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sentiment(polarity=0.36818181818181817, subjectivity=0.7272727272727273)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    }
  ]
}