YT_Comments_sentiment_analysis.ipynb

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              "           author          published_at            updated_at  like_count  \\\n",
              "0         MrBeast  2021-11-24T21:02:45Z  2021-11-24T21:02:45Z      971450   \n",
              "1            熊倉茉衣  2023-09-05T14:28:02Z  2023-09-05T14:28:02Z           1   \n",
              "2    Suwan Yinhok  2023-09-05T13:45:26Z  2023-09-05T13:45:26Z           0   \n",
              "3    Suwan Yinhok  2023-09-05T13:35:02Z  2023-09-05T13:35:02Z           0   \n",
              "4    Suwan Yinhok  2023-09-05T13:31:47Z  2023-09-05T13:31:47Z           0   \n",
              "..            ...                   ...                   ...         ...   \n",
              "91  Fitri Ariella  2023-09-04T02:37:05Z  2023-09-04T02:37:05Z           0   \n",
              "92       georgina  2023-09-04T02:14:16Z  2023-09-04T02:14:16Z           1   \n",
              "93      Dogoplayz  2023-09-04T00:55:00Z  2023-09-04T00:55:00Z           0   \n",
              "94          C & K  2023-09-04T00:19:59Z  2023-09-04T00:19:59Z           2   \n",
              "95         Creeps  2023-09-04T00:12:46Z  2023-09-04T00:12:46Z           1   \n",
              "\n",
              "                                                 text  \n",
              "0   Like I said in the video, subscribe if you hav...  \n",
              "1                                        撮った2枚やったゲーム😊  \n",
              "2                               พระเจ้าเป็นคนส้รางโลก  \n",
              "3                                       เขาแปลเป็นไทย  \n",
              "4                       มิตเตอร์บีสเขาไม่ได้เป็นคนไทย  \n",
              "..                                                ...  \n",
              "91                     Kalo mati ko bisa tepuk tangan  \n",
              "92                Gw rewatch terus sampe 500m viewers  \n",
              "93                                           mr beast  \n",
              "94  Did the person on the slide have blood on thei...  \n",
              "95                                   491.190,653vieas  \n",
              "\n",
              "[96 rows x 5 columns]"
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              "      <td>MrBeast</td>\n",
              "      <td>2021-11-24T21:02:45Z</td>\n",
              "      <td>2021-11-24T21:02:45Z</td>\n",
              "      <td>971450</td>\n",
              "      <td>Like I said in the video, subscribe if you hav...</td>\n",
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              "      <th>1</th>\n",
              "      <td>熊倉茉衣</td>\n",
              "      <td>2023-09-05T14:28:02Z</td>\n",
              "      <td>2023-09-05T14:28:02Z</td>\n",
              "      <td>1</td>\n",
              "      <td>撮った2枚やったゲーム😊</td>\n",
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              "      <th>2</th>\n",
              "      <td>Suwan Yinhok</td>\n",
              "      <td>2023-09-05T13:45:26Z</td>\n",
              "      <td>2023-09-05T13:45:26Z</td>\n",
              "      <td>0</td>\n",
              "      <td>พระเจ้าเป็นคนส้รางโลก</td>\n",
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              "      <th>3</th>\n",
              "      <td>Suwan Yinhok</td>\n",
              "      <td>2023-09-05T13:35:02Z</td>\n",
              "      <td>2023-09-05T13:35:02Z</td>\n",
              "      <td>0</td>\n",
              "      <td>เขาแปลเป็นไทย</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Suwan Yinhok</td>\n",
              "      <td>2023-09-05T13:31:47Z</td>\n",
              "      <td>2023-09-05T13:31:47Z</td>\n",
              "      <td>0</td>\n",
              "      <td>มิตเตอร์บีสเขาไม่ได้เป็นคนไทย</td>\n",
              "    </tr>\n",
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              "      <th>91</th>\n",
              "      <td>Fitri Ariella</td>\n",
              "      <td>2023-09-04T02:37:05Z</td>\n",
              "      <td>2023-09-04T02:37:05Z</td>\n",
              "      <td>0</td>\n",
              "      <td>Kalo mati ko bisa tepuk tangan</td>\n",
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              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>georgina</td>\n",
              "      <td>2023-09-04T02:14:16Z</td>\n",
              "      <td>2023-09-04T02:14:16Z</td>\n",
              "      <td>1</td>\n",
              "      <td>Gw rewatch terus sampe 500m viewers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>Dogoplayz</td>\n",
              "      <td>2023-09-04T00:55:00Z</td>\n",
              "      <td>2023-09-04T00:55:00Z</td>\n",
              "      <td>0</td>\n",
              "      <td>mr beast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>C &amp; K</td>\n",
              "      <td>2023-09-04T00:19:59Z</td>\n",
              "      <td>2023-09-04T00:19:59Z</td>\n",
              "      <td>2</td>\n",
              "      <td>Did the person on the slide have blood on thei...</td>\n",
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              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Creeps</td>\n",
              "      <td>2023-09-04T00:12:46Z</td>\n",
              "      <td>2023-09-04T00:12:46Z</td>\n",
              "      <td>1</td>\n",
              "      <td>491.190,653vieas</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        " import googleapiclient.discovery\n",
        "import googleapiclient.errors\n",
        "\n",
        "api_service_name = \"youtube\"\n",
        "api_version = \"v3\"\n",
        "DEVELOPER_KEY = \"AIzaSyCJqHVRm01j5gPv_MgAOmD2Oomf-QNVdBQ\"\n",
        "\n",
        "youtube = googleapiclient.discovery.build(\n",
        "    api_service_name, api_version, developerKey=DEVELOPER_KEY)\n",
        "\n",
        "request = youtube.commentThreads().list(\n",
        "    part=\"snippet\",\n",
        "    videoId=\"0e3GPea1Tyg\",\n",
        "    maxResults=100\n",
        ")\n",
        "response = request.execute()\n",
        "\n",
        "'''for item in response['items']:\n",
        "    print(item['snippet']['topLevelComment']['snippet']['textDisplay'])'''\n",
        "\n",
        "comments = []\n",
        "\n",
        "for item in response['items']:\n",
        "    comment = item['snippet']['topLevelComment']['snippet']\n",
        "    comments.append([\n",
        "        comment['authorDisplayName'],\n",
        "        comment['publishedAt'],\n",
        "        comment['updatedAt'],\n",
        "        comment['likeCount'],\n",
        "        comment['textDisplay']\n",
        "    ])\n",
        "import pandas as pd\n",
        "df = pd.DataFrame(comments, columns=['author', 'published_at', 'updated_at', 'like_count', 'text'])\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.size"
      ],
      "metadata": {
        "id": "g3BCS6AV7wIX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52183bd4-7f8c-4e76-87e3-9d72293336ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "445"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['text'].values[2]"
      ],
      "metadata": {
        "id": "Cr09OSOfNry0",
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          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "77e63973-4ab8-4284-94ba-ccaf7aa88f99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'still'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "id": "Tt2n_SHuAvwE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "220dc9dc-a4d4-4539-d86c-d0c412cfb269"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(89, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#df[like_count].value_counts()\n",
        "import nltk\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('ggplot')"
      ],
      "metadata": {
        "id": "zx8v8cfSW-YJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.snowball import SnowballStemmer\n",
        "nltk.download('stopwords') #Used for Downloading collection of words\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nOeg4uzpXSSu",
        "outputId": "0196a5f5-ff7f-49c7-a593-43fcea0a88fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sn = SnowballStemmer(\"english\")\n",
        "stop = set(stopwords.words(\"english\"))"
      ],
      "metadata": {
        "id": "Q-eBlk8tYvRV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_text(sms):\n",
        "  sms=sms.lower()\n",
        "  sms = re.sub (\"[^a-z]\", ' ',sms)\n",
        "\n",
        "  sms=nltk.word_tokenize(sms)\n",
        "  sms=[t for t in sms if(len(t)>1)]\n",
        "  sms = [sn.stem(word) for word in sms if word not in stop] #to delete unnecessary words which has no impact on the results\n",
        "  sms = ' '.join(sms)\n",
        "  return sms"
      ],
      "metadata": {
        "id": "SOJjFacCYbLC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['text']=df['text'].apply(clean_text)"
      ],
      "metadata": {
        "id": "JSNwhe1iYjVy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['text'].values[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "-bbOYBMhYqYq",
        "outputId": "5b346c60-c78c-476c-b5bd-f9315d829318"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "''"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eg=df['text'][1]\n",
        "eg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "2hrX8AAYY4Xr",
        "outputId": "5ce0353a-eabc-4475-cdea-8e69ed67dec1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'song good'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.word_tokenize(eg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ehB9k5JNaRRE",
        "outputId": "9da8378e-2dd9-4209-9fb1-8fad44705ed0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['song', 'good']"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.sentiment import SentimentIntensityAnalyzer\n",
        "nltk.download('vader_lexicon')\n",
        "sa=SentimentIntensityAnalyzer()\n",
        "#sa=SentimentIntensityAnalyzer()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_7kign8Wacaa",
        "outputId": "f12b723b-0839-4f66-cf5c-941049478ceb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sa.polarity_scores(\"This is a worst product\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iB8mqoj9dcsU",
        "outputId": "4b61a00a-c4c4-4877-dd58-b209f0824a58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'neg': 0.577, 'neu': 0.423, 'pos': 0.0, 'compound': -0.6249}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#sa.polarity_scores(df['text'])\n",
        "result={}\n",
        "for i in df.index:\n",
        "    result[i]=sa.polarity_scores(df['text'][i])\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qwSUSE4MgZXC",
        "outputId": "3484b169-4e8e-49ad-8f24-79b38926fc19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: {'neg': 0.0, 'neu': 0.442, 'pos': 0.558, 'compound': 0.743}, 1: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 2: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 3: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 4: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 5: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 6: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 7: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 8: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 9: {'neg': 0.0, 'neu': 0.0, 'pos': 1.0, 'compound': 0.5859}, 10: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 11: {'neg': 0.0, 'neu': 0.0, 'pos': 1.0, 'compound': 0.4404}, 12: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 13: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 14: {'neg': 0.0, 'neu': 0.0, 'pos': 1.0, 'compound': 0.4404}, 15: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 16: {'neg': 0.0, 'neu': 0.0, 'pos': 1.0, 'compound': 0.3182}, 17: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 18: {'neg': 0.0, 'neu': 0.286, 'pos': 0.714, 'compound': 0.3612}, 19: {'neg': 0.0, 'neu': 0.0, 'pos': 1.0, 'compound': 0.6369}, 20: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 21: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 22: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 23: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 24: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 25: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 26: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 27: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 28: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 29: {'neg': 0.0, 'neu': 0.0, 'pos': 1.0, 'compound': 0.4404}, 30: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 31: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 32: {'neg': 0.0, 'neu': 0.306, 'pos': 0.694, 'compound': 0.7783}, 33: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 34: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 35: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 36: {'neg': 0.0, 'neu': 0.0, 'pos': 1.0, 'compound': 0.5859}, 37: {'neg': 0.0, 'neu': 0.0, 'pos': 1.0, 'compound': 0.4404}, 38: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 39: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 40: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 41: {'neg': 0.574, 'neu': 0.426, 'pos': 0.0, 'compound': -0.4019}, 42: {'neg': 0.0, 'neu': 0.476, 'pos': 0.524, 'compound': 0.5106}, 43: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 44: {'neg': 0.787, 'neu': 0.213, 'pos': 0.0, 'compound': -0.8126}, 45: {'neg': 0.0, 'neu': 0.667, 'pos': 0.333, 'compound': 0.3612}, 46: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 47: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 48: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 49: {'neg': 0.394, 'neu': 0.606, 'pos': 0.0, 'compound': -0.5994}, 50: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 51: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 52: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 53: {'neg': 0.0, 'neu': 0.312, 'pos': 0.688, 'compound': 0.296}, 54: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 55: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 56: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 57: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 58: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 59: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 60: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 61: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 62: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 63: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 64: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 65: {'neg': 0.0, 'neu': 0.192, 'pos': 0.808, 'compound': 0.6369}, 66: {'neg': 0.0, 'neu': 0.0, 'pos': 1.0, 'compound': 0.6249}, 67: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 68: {'neg': 0.0, 'neu': 0.208, 'pos': 0.792, 'compound': 0.5859}, 69: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 70: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 71: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 72: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 73: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 74: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 75: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 76: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 77: {'neg': 0.0, 'neu': 0.417, 'pos': 0.583, 'compound': 0.6369}, 78: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 79: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 80: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 81: {'neg': 0.0, 'neu': 0.408, 'pos': 0.592, 'compound': 0.4404}, 82: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 83: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 84: {'neg': 0.0, 'neu': 0.323, 'pos': 0.677, 'compound': 0.6369}, 85: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 86: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 87: {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0}, 88: {'neg': 1.0, 'neu': 0.0, 'pos': 0.0, 'compound': -0.128}, 89: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 90: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 91: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 92: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 93: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 94: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}, 95: {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "resultant_df=pd.DataFrame(result).T"
      ],
      "metadata": {
        "id": "94M-GIxZqr0G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "l=[]\n",
        "i=int(input(\"Enter the comment number: \"))\n",
        "l=resultant_df.values[i]\n",
        "print(\"\\nComment: \",df['text'][i])\n",
        "print(\"The given comment is \",round(l[0]*100,2),\"% negative, \",round(l[1]*100,2),\"% neutral, \",round(l[2]*100,2),\"% positive\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h01lcnEpsSei",
        "outputId": "9e1f21a6-0a71-4b20-b5cd-58c98242bb90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the comment number: 0\n",
            "\n",
            "Comment:  like said video subscrib alreadi could win\n",
            "The given comment is  0.0 % negative,  44.2 % neutral,  55.8 % positive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=int(input())\n",
        "df['text'][y]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "x2eVUEl0zdvn",
        "outputId": "e5a87131-6459-412b-f687-ba4b04fbe629"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "''"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "resultant_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "EIGKFsBvxpeT",
        "outputId": "db152a24-a0fc-4587-8957-3ae38aa8ac21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    neg    neu    pos  compound\n",
              "0   0.0  0.442  0.558     0.743\n",
              "1   0.0  0.000  0.000     0.000\n",
              "2   0.0  0.000  0.000     0.000\n",
              "3   0.0  0.000  0.000     0.000\n",
              "4   0.0  0.000  0.000     0.000\n",
              "..  ...    ...    ...       ...\n",
              "91  0.0  1.000  0.000     0.000\n",
              "92  0.0  1.000  0.000     0.000\n",
              "93  0.0  1.000  0.000     0.000\n",
              "94  0.0  1.000  0.000     0.000\n",
              "95  0.0  1.000  0.000     0.000\n",
              "\n",
              "[96 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b6d1aabd-c34a-491e-9788-ce0bb3981f74\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>neg</th>\n",
              "      <th>neu</th>\n",
              "      <th>pos</th>\n",
              "      <th>compound</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.442</td>\n",
              "      <td>0.558</td>\n",
              "      <td>0.743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>96 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b6d1aabd-c34a-491e-9788-ce0bb3981f74')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b6d1aabd-c34a-491e-9788-ce0bb3981f74 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b6d1aabd-c34a-491e-9788-ce0bb3981f74');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b6a025fb-3413-4e94-8486-ed36541ce03b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b6a025fb-3413-4e94-8486-ed36541ce03b')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b6a025fb-3413-4e94-8486-ed36541ce03b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = resultant_df.mean(axis = 0, skipna = False)\n",
        "df2=round(df2*100,2)\n",
        "final= pd.Series(df2)\n",
        "final"
      ],
      "metadata": {
        "id": "L1Xan4ZDwF84",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4aa0f71e-a6a5-43b3-c0e9-6944167c741a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neg          2.87\n",
              "neu         44.04\n",
              "pos         16.63\n",
              "compound     8.92\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"The given video has: \\n \",\"\\n\",round(final[2]),\"% positive comments\\n \",\n",
        "      round(final[0]),\"% negative comments\\n\",\n",
        "      round(final[1]),\"% neutral comments\\n\")\n",
        "print(\"-----------------------------------------------------------------------\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eMd-LSsPzvUa",
        "outputId": "9fd70ddd-ce9a-4769-8173-047fe5392845"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The given video has: \n",
            "  \n",
            " 17 % positive comments\n",
            "  3 % negative comments\n",
            " 44 % neutral comments\n",
            "\n",
            "-----------------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Nr8uYHqL1FWX"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}