VADER_Vs_TextBlob.ipynb

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      "name": "VADER Vs TextBlob.ipynb",
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      "cell_type": "markdown",
      "source": [
        "# VADER Vs. TextBlob - Which One Is Better For Social Media Sentiment Analysis?\n",
        "\n",
        "@Author: [Zoumana KEITA](zoumanakeita.medium.com/)"
      ],
      "metadata": {
        "id": "pSoubxIA-Jus"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## VADER"
      ],
      "metadata": {
        "id": "CVfzoKLc-0fP"
      }
    },
    {
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      "execution_count": 2,
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk) (1.15.0)\n",
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n"
          ]
        },
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/nltk/twitter/__init__.py:20: UserWarning: The twython library has not been installed. Some functionality from the twitter package will not be available.\n",
            "  warnings.warn(\"The twython library has not been installed. \"\n"
          ]
        }
      ],
      "source": [
        "##### VADER PREREQUISITES\n",
        "\n",
        "# Install and import nltk\n",
        "!pip install nltk\n",
        "import nltk\n",
        "\n",
        "# Download the lexicon\n",
        "nltk.download(\"vader_lexicon\")\n",
        "\n",
        "# Import the lexicon \n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "\n",
        "# Create an instance of SentimentIntensityAnalyzer\n",
        "sent_analyzer = SentimentIntensityAnalyzer()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sentence = \"VADER is pretty good at identifying the underlying sentiment of a text!\"\n",
        "print(sent_analyzer.polarity_scores(sentence))"
      ],
      "metadata": {
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        "id": "sYzivw6l-2zF",
        "outputId": "8ae5dd4f-4517-4fa0-9b7e-24ea2a8d4803"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'neg': 0.0, 'neu': 0.585, 'pos': 0.415, 'compound': 0.75}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## TextBlob"
      ],
      "metadata": {
        "id": "5IvqJiUV-4lu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "##### TextBlob PREREQUISITES\n",
        "!pip install textblob\n",
        "from textblob import TextBlob"
      ],
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      "execution_count": 3,
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          "name": "stdout",
          "text": [
            "Requirement already satisfied: textblob in /usr/local/lib/python3.7/dist-packages (0.15.3)\n",
            "Requirement already satisfied: nltk>=3.1 in /usr/local/lib/python3.7/dist-packages (from textblob) (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk>=3.1->textblob) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a textblob object\n",
        "sentence = TextBlob(\"Humans bad actions are destroying our only a one planet\")\n",
        "\n",
        "# Get the prediction/sentiment\n",
        "print(sentence.sentiment)"
      ],
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        "outputId": "d91707b6-2b09-4227-cdf0-6129c22354c1"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentiment(polarity=-0.3, subjectivity=0.5555555555555555)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Benchmark Analysis "
      ],
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      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Read the data set\n",
        "data_url = \"https://raw.githubusercontent.com/keitazoumana/VADER_sentiment-Analysis/main/data/testdata.manual.2009.06.14.csv\"\n",
        "sentiment_data = pd.read_csv(data_url)\n",
        "\n",
        "# Show 3 random rows\n",
        "sentiment_data.sample(3)"
      ],
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              "     4     3  Mon May 11 03:17:40 UTC 2009                kindle2  \\\n",
              "355  4  2223  Wed May 27 23:56:46 UTC 2009               googleio   \n",
              "132  4   397  Tue Jun 02 02:55:49 UTC 2009  \"night at the museum\"   \n",
              "103  0   227  Mon May 25 17:46:06 UTC 2009            twitter api   \n",
              "\n",
              "           tpryan  \\\n",
              "355        daynah   \n",
              "132  jeremyempire   \n",
              "103        fwhamm   \n",
              "\n",
              "    @stellargirl I loooooooovvvvvveee my Kindle2. Not that the DX is cool, but the 2 is fantastic in its own right.  \n",
              "355  HTML 5 Demos! Lots of great stuff to come! Yes...                                                               \n",
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      "source": [
        "### Data Preprocessing"
      ],
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      "source": [
        "def format_data(data):\n",
        "\n",
        "  last_col = str(data.columns[-1])\n",
        "  first_col = str(data.columns[0])\n",
        "\n",
        "  data.rename(columns = {last_col: 'tweet_text', first_col: 'polarity'}, inplace=True) \n",
        "\n",
        "  # Change 0, 2, 4 to negative, neutral and positive\n",
        "  labels = {0: 'negative', 2: 'neutral', 4: 'positive'}\n",
        "  data['polarity'] = data['polarity'].map(labels)\n",
        "\n",
        "  # Get only the two columns\n",
        "  return data[['tweet_text', 'polarity']]"
      ],
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      "execution_count": 7,
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        "# Apply the transformation\n",
        "data = format_data(sentiment_data)\n",
        "data.sample(3)"
      ],
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      "execution_count": 8,
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              "                                            tweet_text  polarity\n",
              "278  RT @justindavey: RT @tweetmeme GM OnStar now i...   neutral\n",
              "325                        LAKERS tonight let's go!!!!  positive\n",
              "26   Booz Allen Hamilton has a bad ass homegrown so...  positive"
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              "                                                     [key], {});\n",
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    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "khoAl4AF_L1S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Comparative Analysis"
      ],
      "metadata": {
        "id": "oEDgylDi_Q4p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Helper Functions"
      ],
      "metadata": {
        "id": "CtWPyHMp_T-O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def format_VADER_output(output_dict):\n",
        "  \n",
        "  polarity = \"neutral\"\n",
        "\n",
        "  if(output_dict['compound']>= 0.05):\n",
        "    polarity = \"positive\"\n",
        "\n",
        "  elif(output_dict['compound']<= -0.05):\n",
        "    polarity = \"negative\"\n",
        "\n",
        "  return polarity\n",
        "\n",
        "def predict_VADER_sentiment(text):\n",
        "  \n",
        "  output_dict =  sent_analyzer.polarity_scores(text)\n",
        "  return format_VADER_output(output_dict)"
      ],
      "metadata": {
        "id": "o7u8_zMK_SkG"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def format_TextBlob_output(prediction):\n",
        "  \n",
        "  polarity = \"neutral\"\n",
        "\n",
        "  if(prediction.polarity > 0):\n",
        "    polarity = \"positive\"\n",
        "\n",
        "  elif(prediction.polarity < 0):\n",
        "    polarity = \"negative\"\n",
        "\n",
        "  return polarity\n",
        "\n",
        "def predict_Textblob_sentiment(text):\n",
        "  \n",
        "  text_blob_prediction =  TextBlob(text).sentiment\n",
        "  return format_TextBlob_output(text_blob_prediction)"
      ],
      "metadata": {
        "id": "123Zrro3_YhY"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "8PmjNVqu_ZIy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Predictions Result"
      ],
      "metadata": {
        "id": "ygy0CNDz_b1S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "## VADER SECTION\n",
        "data[\"vader_prediction\"] = data[\"tweet_text\"].apply(predict_VADER_sentiment)\n",
        "vader_accuracy = accuracy_score(data['polarity'], data['vader_prediction'])\n",
        "\n",
        "## TextBlob SECTIOB\n",
        "data[\"textblob_prediction\"] = data[\"tweet_text\"].apply(predict_Textblob_sentiment)\n",
        "textblob_accuracy = accuracy_score(data['polarity'], data['textblob_prediction'])\n",
        "\n",
        "\n",
        "## Print the results\n",
        "print(\"Accuracy Scores\")\n",
        "print(\"- VADER: {}\".format(vader_accuracy))\n",
        "print(\"- Textblob: {}\".format(textblob_accuracy))\n",
        "print(\"-\"*50)\n",
        "print(\"Classification report\")\n",
        "print(\"- VADER\")\n",
        "print(classification_report(data['polarity'], data['vader_prediction']))\n",
        "print(\"\\n\")\n",
        "\n",
        "print(\"- TextBlob\")\n",
        "print(classification_report(data['polarity'], data['textblob_prediction']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rk7UPqvb_dmi",
        "outputId": "25b43881-fc06-4cb6-ec24-a974a63f451a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Scores\n",
            "- VADER: 0.716297786720322\n",
            "- Textblob: 0.6498993963782697\n",
            "--------------------------------------------------\n",
            "Classification report\n",
            "- VADER\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.84      0.64      0.72       177\n",
            "     neutral       0.66      0.70      0.68       139\n",
            "    positive       0.68      0.81      0.74       181\n",
            "\n",
            "    accuracy                           0.72       497\n",
            "   macro avg       0.73      0.71      0.71       497\n",
            "weighted avg       0.73      0.72      0.72       497\n",
            "\n",
            "\n",
            "\n",
            "- TextBlob\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.80      0.49      0.60       177\n",
            "     neutral       0.61      0.68      0.64       139\n",
            "    positive       0.61      0.79      0.69       181\n",
            "\n",
            "    accuracy                           0.65       497\n",
            "   macro avg       0.67      0.65      0.64       497\n",
            "weighted avg       0.68      0.65      0.64       497\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "tG74YSaI_d_b"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}