vader_sentiment_analyzer.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**below code used to install VaderSentiment module**"
      ],
      "metadata": {
        "id": "o3xgGlpxkocR"
      }
    },
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        "outputId": "c5cf04bd-0de5-45f1-da3e-aa8bb7c32afb"
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: vaderSentiment in /usr/local/lib/python3.8/dist-packages (3.3.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from vaderSentiment) (2.23.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->vaderSentiment) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->vaderSentiment) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->vaderSentiment) (2022.9.24)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->vaderSentiment) (2.10)\n"
          ]
        }
      ],
      "source": [
        "pip install vaderSentiment"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
        "obj = SentimentIntensityAnalyzer()"
      ],
      "metadata": {
        "id": "U_6XjJ1M92Fq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "vh2mKdZACqTg",
        "outputId": "64cfe8dd-a397-4520-ee22-c85c01907d68",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/TCD - TCD.csv')"
      ],
      "metadata": {
        "id": "d1IVbOPD-Q1O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**below code is to select specified column**"
      ],
      "metadata": {
        "id": "I_fRo0P_kz44"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = data[\"Opinion\"]"
      ],
      "metadata": {
        "id": "xLYVAmIz-hhN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
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        },
        "id": "yA-uqzsm-mML",
        "outputId": "d271ffd9-baf7-40d8-e489-b0b0914ba750"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Anxiety about social consequences; Appeal to p...\n",
              "1    Recruitment of Gorkha soldiers will continue u...\n",
              "2    All petitions related to Agneepath Recruitment...\n",
              "3    Rajnath Singh - Agneepath: Rajnath Singh will ...\n",
              "4    Positional Buy cmp 16.80. 12.80% up so far. st...\n",
              "Name: Opinion, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**below code is to analise the pros and cons of data**"
      ],
      "metadata": {
        "id": "VNz3elX3k9Fp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "neutral=0\n",
        "negative=0\n",
        "positive=0\n",
        "for i in range(len(data)):\n",
        "  try:\n",
        "    \n",
        "    sentence = data[i]\n",
        "    if \"Justice\" or \"justice\" or \"against\" or \"protest\" not in sentence:\n",
        "     \n",
        "      sentiment_dict = obj.polarity_scores(sentence)\n",
        "    \n",
        "    \n",
        "      if(sentiment_dict['compound']>0.5):\n",
        "        positive+=1\n",
        "        \n",
        "      elif(sentiment_dict['compound']<0):\n",
        "        negative+=1\n",
        "      else:\n",
        "        neutral+=1\n",
        "    else:\n",
        "      negative+=1\n",
        "  except:\n",
        "        pass\n",
        "print(\"positive :\",positive,\" neutral : \",neutral,\" negative : \",negative)\n",
        "  \n",
        "      \n",
        "\n",
        "  \n",
        "  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B_sB6hIL-rTV",
        "outputId": "a33797e6-dd35-4a38-dd5a-1bc9727ae13b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "positive : 11566  neutral :  54141  negative :  18822\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**below code is to create a dataframe for visualization**"
      ],
      "metadata": {
        "id": "Rpv4fW7XlOey"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import  seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "data=pd.DataFrame({\n",
        "    \"type_of_comment\":[\"positive\",\"negative\",\"neutral\"],\n",
        "    \"votes\":[positive,negative,neutral]\n",
        "})\n",
        "\n",
        "data"
      ],
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        "outputId": "8d586972-2202-4a04-fbe5-026bf979ced9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  type_of_comment  votes\n",
              "0        positive  11566\n",
              "1        negative  18822\n",
              "2         neutral  54141"
            ],
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              "      <th></th>\n",
              "      <th>type_of_comment</th>\n",
              "      <th>votes</th>\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "**below we analised by drawing pie chart and bar graph**"
      ],
      "metadata": {
        "id": "Ft4q0bJulaYv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby(['type_of_comment']).sum().plot(kind='pie', y='votes')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "cFdT7H7EJgVZ",
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      "outputs": [
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby(['type_of_comment']).sum().plot(kind='bar', y='votes')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "AkM7JO-cZUM6",
        "outputId": "2010c9f9-ca99-4e6d-8473-f83b1a089a72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "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": [
        "data=data.drop(2)"
      ],
      "metadata": {
        "id": "aHqufh87KEpg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**we have analised for only positive reviews and negative ones**"
      ],
      "metadata": {
        "id": "Lo5EiQkomBgd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby(['type_of_comment']).sum().plot(kind='pie', y='votes')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "3JtxE1GdQDJ8",
        "outputId": "f010a51d-b2dd-4bdc-d765-03eca9410778",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby(['type_of_comment']).sum().plot(kind='bar', y='votes')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "PH3HlKAWYzHA",
        "outputId": "0886c51b-ba95-4edc-cfe1-355f0d63cc9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "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": [],
      "metadata": {
        "id": "v27H4Uz4mcZ2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}