BitCoin_Major_Project_Extractor.ipynb

{
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/kiran-lenka/Bitcoin-Price-Predictor/blob/main/BitCoin_Major_Project_Extractor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KsTjTCTfQJxs"
      },
      "source": [
        "## Importing and Installing Requiremnts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "JZ9HbgDp8M9L",
        "outputId": "4616f185-ca26-49e3-dd8e-84ee0e60636e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "!pip install -q praw\n",
        "\n",
        "# importing the necessary libraries\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import praw\n",
        "import tweepy as tw\n",
        "\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import ssl  \n",
        "import os\n",
        "from datetime import date\n",
        "from io import StringIO\n",
        "import json\n",
        "\n",
        "import nltk \n",
        "nltk.download('wordnet')\n",
        "nltk.download('vader_lexicon')\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer as SIA\n",
        "from nltk.stem import WordNetLemmatizer, SnowballStemmer\n",
        "from nltk.stem.porter import *\n",
        "\n",
        "import gensim\n",
        "from gensim.utils import simple_preprocess\n",
        "from gensim.parsing.preprocessing import STOPWORDS\n",
        "\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "import plotly.figure_factory as ff\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KQbAuYpqYs-B"
      },
      "source": [
        "# Bitcoin Market Sentiment Analysis and Prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uGcz7rXV1CMI"
      },
      "source": [
        "## Calculating public sentiment on Bitcoin"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vEEyPBLukNbn"
      },
      "source": [
        "### Reddit Sentiment Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dOHXsNmi8TZA"
      },
      "outputs": [],
      "source": [
        "# reddit credentials\n",
        "reddit = praw.Reddit(client_id = 'UYBiraXAwH8bcw',\n",
        "                     client_secret = 'RMg2VFM9ncuAwLl61YB301SBfTZkUQ',\n",
        "                     user_agent = 'MyAPI/0.0.1',\n",
        "                     check_for_async=False\n",
        "                    )\n",
        "\n",
        "# getting posts from the subreddits\n",
        "lst_reddit = []\n",
        "\n",
        "# bitcoin subreddit\n",
        "subreddit = reddit.subreddit('bitcoin')\n",
        "# hot posts\n",
        "for post in subreddit.hot(limit=1000):\n",
        "  lst_reddit.append(post.title)\n",
        "# new posts\n",
        "for post in subreddit.new(limit=1000):\n",
        "  lst_reddit.append(post.title)\n",
        "\n",
        "# CryptoCurrency subreddit\n",
        "subreddit = reddit.subreddit('CryptoCurrency')\n",
        "# hot posts\n",
        "for post in subreddit.hot(limit=1000):\n",
        "  lst_reddit.append(post.title)\n",
        "# new posts\n",
        "for post in subreddit.new(limit=1000):\n",
        "  lst_reddit.append(post.title)\n",
        "\n",
        "# btc subreddit\n",
        "subreddit = reddit.subreddit('btc')\n",
        "# hot posts\n",
        "for post in subreddit.hot(limit=1000):\n",
        "  lst_reddit.append(post.title)\n",
        "# new posts\n",
        "for post in subreddit.new(limit=1000):\n",
        "  lst_reddit.append(post.title)\n",
        "\n",
        "# Crypto_General subreddit\n",
        "subreddit = reddit.subreddit('Crypto_General')\n",
        "# hot posts\n",
        "for post in subreddit.hot(limit=1000):\n",
        "  lst_reddit.append(post.title)\n",
        "# new posts\n",
        "for post in subreddit.new(limit=1000):\n",
        "  lst_reddit.append(post.title)\n",
        "\n",
        "# Coinbase subreddit\n",
        "subreddit = reddit.subreddit('Coinbase')\n",
        "# hot posts\n",
        "for post in subreddit.hot(limit=1000):\n",
        "  lst_reddit.append(post.title)\n",
        "# new posts\n",
        "for post in subreddit.new(limit=1000):\n",
        "  lst_reddit.append(post.title)\n",
        "\n",
        "# Binance subreddit\n",
        "subreddit = reddit.subreddit('Binance')\n",
        "# hot posts\n",
        "for post in subreddit.hot(limit=1000):\n",
        "  lst_reddit.append(post.title)\n",
        "# new posts\n",
        "for post in subreddit.new(limit=1000):\n",
        "  lst_reddit.append(post.title)\n",
        " \n",
        "# converting the list into a dataframe and displaying it \n",
        "df_reddit = pd.DataFrame(lst_reddit, columns=['Post Titles'])"
      ]
    },
    {
      "cell_type": "code",
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        "outputId": "10212dcc-7e29-4481-a93d-ffc58e0a1314"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of rows (Reddit posts) in the dataset: 8030\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         Post Titles\n",
              "0  Mentor Monday, April 25, 2022: Ask all your bi...\n",
              "1                   Daily Discussion, April 25, 2022\n",
              "2  BREAKING: The Central African Republic, became...\n",
              "3  Mt. Hodl: 64% of all bitcoin has not moved in ...\n",
              "4                   At the stoplight in front of me."
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Post Titles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mentor Monday, April 25, 2022: Ask all your bi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Daily Discussion, April 25, 2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BREAKING: The Central African Republic, became...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Mt. Hodl: 64% of all bitcoin has not moved in ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>At the stoplight in front of me.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          },
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        }
      ],
      "source": [
        "print(\"No. of rows (Reddit posts) in the dataset:\",len(df_reddit))\n",
        "df_reddit.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IdT-qUnVOXW5"
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      "source": [
        "# classifying the post as positive, negative or neutral and displaying the results\n",
        "sia = SIA()\n",
        "results = []\n",
        "for line in lst_reddit:\n",
        "  pol_score = sia.polarity_scores(line)\n",
        "  pol_score['Post Titles'] = line\n",
        "  results.append(pol_score)\n",
        "\n",
        "df_reddit_nlp = pd.DataFrame(results)\n",
        "\n",
        "# compound is taken as the deciding factor is classifying the sentiment\n",
        "\n",
        "# positive\n",
        "df_reddit_nlp.loc[df_reddit_nlp['compound'] > 0.05, 'Sentiment'] = '1'\n",
        "\n",
        "# negative\n",
        "df_reddit_nlp.loc[df_reddit_nlp['compound'] < -0.05, 'Sentiment'] = '-1'\n",
        "\n",
        "# neutral\n",
        "df_reddit_nlp.loc[(df_reddit_nlp['compound'] >= -0.05) & (df_reddit_nlp['compound'] <= 0.05), 'Sentiment'] = '0'"
      ]
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          "data": {
            "text/plain": [
              "   neg    neu    pos  compound  \\\n",
              "0  0.0  1.000  0.000     0.000   \n",
              "1  0.0  1.000  0.000     0.000   \n",
              "2  0.0  0.903  0.097     0.128   \n",
              "3  0.0  1.000  0.000     0.000   \n",
              "4  0.0  1.000  0.000     0.000   \n",
              "\n",
              "                                         Post Titles Sentiment  \n",
              "0  Mentor Monday, April 25, 2022: Ask all your bi...         0  \n",
              "1                   Daily Discussion, April 25, 2022         0  \n",
              "2  BREAKING: The Central African Republic, became...         1  \n",
              "3  Mt. Hodl: 64% of all bitcoin has not moved in ...         0  \n",
              "4                   At the stoplight in front of me.         0  "
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              "      <th>pos</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.000</td>\n",
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              "      <td>0.097</td>\n",
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              "            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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "df_reddit_nlp.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DcE-REKoyEHP"
      },
      "outputs": [],
      "source": [
        "df_reddit_nlp.to_csv('reddit-senti.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1-ty72FkRTE"
      },
      "source": [
        "### Twitter Sentiment Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bzmd4lrEkTa1",
        "outputId": "da1dd641-2e96-4683-fee7-e1f2009a50cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 26.5 s, sys: 2.62 s, total: 29.1 s\n",
            "Wall time: 50min 1s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "consumer_key= 'uLPC3KfMtGFcEeq4CxEOohZeg'\n",
        "consumer_secret= 'tywsJRvcr2zz5ICg7bkadbSIIjhGFmAlOLjJECjPqMfaRuwc1T'\n",
        "access_token= '1300465599823314944-VkC6tWnEUrbxTZ1wYpWIxbc8LQCPNL'\n",
        "access_token_secret= 'DDiF0cmidxoQlT2rgEUCGkP4E2DI8PBwz6WMS5QL51zOG'\n",
        "auth = tw.OAuthHandler(consumer_key, consumer_secret)\n",
        "auth.set_access_token(access_token, access_token_secret)\n",
        "api = tw.API(auth, wait_on_rate_limit=True)\n",
        "\n",
        "search_words = ['#crypto', '#bitcoin','#cryptocurrency','#bitcoins']\n",
        "date_since = '2021-05-01'\n",
        "tweet_text = []\n",
        "date_time = []\n",
        "location = []\n",
        "\n",
        "for words in search_words:\n",
        "  tweets = tw.Cursor(api.search,\n",
        "              q=words,\n",
        "              lang=\"en\",\n",
        "              since=date_since).items(2000)\n",
        "  for tweet in tweets:\n",
        "    str1 = tweet.text\n",
        "    str2 = tweet.created_at\n",
        "    str3 = tweet.user.location\n",
        "    tweet_text.append(str1)\n",
        "    date_time.append(str2)\n",
        "    location.append(str3)\n",
        "\n",
        "df_twitter = pd.DataFrame()\n",
        "df_twitter['Tweets'] = tweet_text\n",
        "df_twitter['Created at'] = date_time\n",
        "df_twitter['Location'] = location"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QJD7y5cIUXNP",
        "outputId": "3689bc57-8f18-4561-cb3b-629ae7f64f83"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of rows (tweets) in the dataset: 8000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              Tweets          Created at  \\\n",
              "0  RT @NFT_Holding: FREE #NFT GIVEAWAY!! 🎉\\n\\nRT ... 2022-04-25 09:52:39   \n",
              "1  RT @ShibnobiNews: Hey #Shibnobi is a beautiful... 2022-04-25 09:52:39   \n",
              "2  RT @GyroCommunity: First GameFi on @PlatON_Net... 2022-04-25 09:52:39   \n",
              "3  RT @ICThanat0s: 📢NFT GIVEAWAY!📢\\nWe've teamed ... 2022-04-25 09:52:39   \n",
              "4  @CryptoTownEU Good Project \\n@TahsinAhamedNil\\... 2022-04-25 09:52:38   \n",
              "\n",
              "                Location  \n",
              "0                         \n",
              "1  Hồ Chí Minh, Việt Nam  \n",
              "2               Dirumah   \n",
              "3                Nigeria  \n",
              "4                         "
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tweets</th>\n",
              "      <th>Created at</th>\n",
              "      <th>Location</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>RT @NFT_Holding: FREE #NFT GIVEAWAY!! 🎉\\n\\nRT ...</td>\n",
              "      <td>2022-04-25 09:52:39</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RT @ShibnobiNews: Hey #Shibnobi is a beautiful...</td>\n",
              "      <td>2022-04-25 09:52:39</td>\n",
              "      <td>Hồ Chí Minh, Việt Nam</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RT @GyroCommunity: First GameFi on @PlatON_Net...</td>\n",
              "      <td>2022-04-25 09:52:39</td>\n",
              "      <td>Dirumah</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RT @ICThanat0s: 📢NFT GIVEAWAY!📢\\nWe've teamed ...</td>\n",
              "      <td>2022-04-25 09:52:39</td>\n",
              "      <td>Nigeria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>@CryptoTownEU Good Project \\n@TahsinAhamedNil\\...</td>\n",
              "      <td>2022-04-25 09:52:38</td>\n",
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              "            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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "print(\"No. of rows (tweets) in the dataset:\",len(df_twitter))\n",
        "df_twitter.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nyYCIT0i1T_v"
      },
      "outputs": [],
      "source": [
        "sia = SIA()\n",
        "results = []\n",
        "for line in tweet_text:\n",
        "  pol_score = sia.polarity_scores(line)\n",
        "  pol_score['Tweets'] = line\n",
        "  results.append(pol_score)\n",
        "\n",
        "df_twitter_nlp = pd.DataFrame(results)\n",
        "\n",
        "# compound is taken as the deciding factor is classifying the sentiment\n",
        "\n",
        "# positive\n",
        "df_twitter_nlp.loc[df_twitter_nlp['compound'] > 0.05, 'Sentiment'] = '1'\n",
        "\n",
        "# negative\n",
        "df_twitter_nlp.loc[df_twitter_nlp['compound'] < -0.05, 'Sentiment'] = '-1'\n",
        "\n",
        "# neutral\n",
        "df_twitter_nlp.loc[(df_twitter_nlp['compound'] <= 0.05) & (df_twitter_nlp['compound'] >= -0.05) , 'Sentiment'] = '0'\n",
        "\n",
        "df_twitter_nlp['Created at'] = date_time\n",
        "df_twitter_nlp['Location'] = location"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FL6eeo5B_Kzx",
        "outputId": "291de9f0-887e-41a2-96a7-db7c0524567a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   neg    neu    pos  compound  \\\n",
              "0  0.0  0.765  0.235    0.7104   \n",
              "1  0.0  0.843  0.157    0.5994   \n",
              "2  0.0  0.888  0.112    0.3400   \n",
              "3  0.0  1.000  0.000    0.0000   \n",
              "4  0.0  0.734  0.266    0.4404   \n",
              "\n",
              "                                              Tweets Sentiment  \\\n",
              "0  RT @NFT_Holding: FREE #NFT GIVEAWAY!! 🎉\\n\\nRT ...         1   \n",
              "1  RT @ShibnobiNews: Hey #Shibnobi is a beautiful...         1   \n",
              "2  RT @GyroCommunity: First GameFi on @PlatON_Net...         1   \n",
              "3  RT @ICThanat0s: 📢NFT GIVEAWAY!📢\\nWe've teamed ...         0   \n",
              "4  @CryptoTownEU Good Project \\n@TahsinAhamedNil\\...         1   \n",
              "\n",
              "           Created at               Location  \n",
              "0 2022-04-25 09:52:39                         \n",
              "1 2022-04-25 09:52:39  Hồ Chí Minh, Việt Nam  \n",
              "2 2022-04-25 09:52:39               Dirumah   \n",
              "3 2022-04-25 09:52:39                Nigeria  \n",
              "4 2022-04-25 09:52:38                         "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>neg</th>\n",
              "      <th>neu</th>\n",
              "      <th>pos</th>\n",
              "      <th>compound</th>\n",
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              "      <td>RT @NFT_Holding: FREE #NFT GIVEAWAY!! 🎉\\n\\nRT ...</td>\n",
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              "      <td>Hồ Chí Minh, Việt Nam</td>\n",
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              "      <td>1</td>\n",
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              "      <td>Dirumah</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0000</td>\n",
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              "      <td>0</td>\n",
              "      <td>2022-04-25 09:52:39</td>\n",
              "      <td>Nigeria</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.734</td>\n",
              "      <td>0.266</td>\n",
              "      <td>0.4404</td>\n",
              "      <td>@CryptoTownEU Good Project \\n@TahsinAhamedNil\\...</td>\n",
              "      <td>1</td>\n",
              "      <td>2022-04-25 09:52:38</td>\n",
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              "          const element = document.querySelector('#df-f4ad413a-9184-431c-b05f-db6573ba3e3c');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "df_twitter_nlp.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1dOyclT5e2-w",
        "outputId": "07457611-e47f-4166-adaf-7b855b8d40e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8000"
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          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "len(df_twitter_nlp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MQJODOTtfek3",
        "outputId": "8bd24d0c-67c5-41ad-8942-61e6622fce92"
      },
      "outputs": [
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          "data": {
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              "       neg    neu    pos  compound  \\\n",
              "0     0.00  0.765  0.235    0.7104   \n",
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              "2     0.00  0.888  0.112    0.3400   \n",
              "3     0.00  1.000  0.000    0.0000   \n",
              "4     0.00  0.734  0.266    0.4404   \n",
              "...    ...    ...    ...       ...   \n",
              "7995  0.15  0.850  0.000   -0.3182   \n",
              "7996  0.00  1.000  0.000    0.0000   \n",
              "7997  0.00  1.000  0.000    0.0000   \n",
              "7998  0.00  0.898  0.102    0.1779   \n",
              "7999  0.00  1.000  0.000    0.0000   \n",
              "\n",
              "                                                 Tweets Sentiment  \\\n",
              "0     RT @NFT_Holding: FREE #NFT GIVEAWAY!! 🎉\\n\\nRT ...         1   \n",
              "1     RT @ShibnobiNews: Hey #Shibnobi is a beautiful...         1   \n",
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              "3     RT @ICThanat0s: 📢NFT GIVEAWAY!📢\\nWe've teamed ...         0   \n",
              "4     @CryptoTownEU Good Project \\n@TahsinAhamedNil\\...         1   \n",
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              "7995  Crypto and Bitcoin KYC: Everything Critical Yo...        -1   \n",
              "7996  Here we go...\\n\\n#Bitcoin 43k is next from cur...         0   \n",
              "7997  #Bitcoin supply audit!\\nTotal #Bitcoins mined ...         0   \n",
              "7998  @baalazamon @SolanaFndn @ProjectSerum @PythNet...         1   \n",
              "7999  RT @GabrielsChrista: Open account with Binance...         0   \n",
              "\n",
              "              Created at  \n",
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              "2    2022-04-25 09:52:39  \n",
              "3    2022-04-25 09:52:39  \n",
              "4    2022-04-25 09:52:38  \n",
              "...                  ...  \n",
              "7995 2022-04-21 00:30:00  \n",
              "7996 2022-04-21 00:26:08  \n",
              "7997 2022-04-21 00:22:06  \n",
              "7998 2022-04-21 00:21:39  \n",
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              "      <td>RT @NFT_Holding: FREE #NFT GIVEAWAY!! 🎉\\n\\nRT ...</td>\n",
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              "      <td>2022-04-25 09:52:39</td>\n",
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              "      <td>2022-04-25 09:52:39</td>\n",
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              "      <td>RT @ICThanat0s: 📢NFT GIVEAWAY!📢\\nWe've teamed ...</td>\n",
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              "      <td>2022-04-25 09:52:39</td>\n",
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              "      <td>@CryptoTownEU Good Project \\n@TahsinAhamedNil\\...</td>\n",
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              "      <td>1.000</td>\n",
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              "      <td>0.0000</td>\n",
              "      <td>Here we go...\\n\\n#Bitcoin 43k is next from cur...</td>\n",
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          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "df_twitter_nlp.drop(columns = ['Location'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BFnIRSVs-cuI"
      },
      "outputs": [],
      "source": [
        "df_twitter_nlp.to_csv('twitter-senti.csv')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "BitCoin Major Project - Extractor",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "name": "python"
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  "nbformat_minor": 0
}