Data_Analysis.ipynb

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      "name": "Crypto-currency Sentiment Analysis.ipynb",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "j17YN_MotOJ0"
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      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQ-QiKQ4s903"
      },
      "source": [
        "import glob\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import requests\n",
        "#import html5lib\n",
        "from bs4 import BeautifulSoup\n",
        "import datetime\n",
        "import re"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5-kl_4Mwknp"
      },
      "source": [
        "def percentage(part, whole):\n",
        "    return 100 * float(part)/float(whole)"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YwN7nzW8ty7o"
      },
      "source": [
        "# !ls '/content/drive/My Drive/database'\n",
        "# these paths are configured to read the database from google drive\n",
        "tweets_path = '/content/drive/My Drive/database/Tweets/bitcoin/'\n",
        "bitcoin_file = '/content/drive/My Drive/database/bitcoinHistoricalData/bitcoin.csv'\n",
        "crypto_name = 'bitcoin' # bitcoin, ethereum, bitcoincash, ripple or litecoin\n"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kg8VdSBPs91T",
        "outputId": "3586d825-cd83-412b-c187-4b69043cfe92",
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          "height": 264
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      },
      "source": [
        "# initializing dataframes\n",
        "df_noOfTweets = pd.DataFrame(columns=['date','positive', 'neutral', 'negative', 'total']) # for the barchart of no. of tweets\n",
        "df_polarity_line = pd.DataFrame(columns=['date','polarity']) # for the polarity line chart\n",
        "\n",
        "# Tweets about each crypto is taken from saved txt files with dates ranging from 2017-09-01 to 2017-11-30\n",
        "# Tweets are collected by selenium automated script written in python\n",
        "for f in glob.glob( str(tweets_path) + crypto_name + '*.txt'):\n",
        "    df_datawise = pd.read_csv(f, sep='\\t', encoding='utf-8', parse_dates=True)\n",
        "    polarity = 0.00\n",
        "    neutral = positive = negative = 0\n",
        "    for idx,row in df_datawise.iterrows():\n",
        "        polarity += float(row[\"polarity\"])\n",
        "        \n",
        "        # adding reaction of how people are reacting to find average later\n",
        "        if (float(row[\"polarity\"]) >= -0.1 and float(row[\"polarity\"]) <= 0.1):  \n",
        "            neutral += 1\n",
        "        elif (float(row[\"polarity\"]) > 0.1):\n",
        "            positive += 1\n",
        "        elif (float(row[\"polarity\"]) <-0.1):\n",
        "            negative += 1\n",
        "    positive = percentage(positive, len(df_datawise.index))\n",
        "    negative = percentage(negative, len(df_datawise.index))\n",
        "    neutral = percentage(neutral, len(df_datawise.index))\n",
        "    # below df format is for barchart with sentiments\n",
        "    df_temp_df_noOfTweets = pd.DataFrame({'date': df_datawise.iloc[0]['date'], 'positive': ((positive/100)*len(df_datawise.index)),'neutral': ((neutral/100)*len(df_datawise.index)), 'negative': ((negative/100)*len(df_datawise.index)), 'total': len(df_datawise.index)}, index=[0])\n",
        "    # print(df_temp_df_noOfTweets)\n",
        "    # polarity /= df_temp_df_noOfTweets['total']\n",
        "    df_noOfTweets = df_noOfTweets.append(df_temp_df_noOfTweets,sort=True, ignore_index=True)\n",
        "    \n",
        "    # below df format is for polarity_line with sentiments\n",
        "    df_temp_polarity_line = pd.DataFrame({'date': df_datawise.iloc[0]['date'], 'polarity': polarity}, index=[0])\n",
        "    df_polarity_line = df_polarity_line.append(df_temp_polarity_line, sort=True, ignore_index=True)\n",
        "\n",
        "df_noOfTweets = df_noOfTweets.sort_values(by = 'date')\n",
        "df_noOfTweets.reset_index(inplace=True)\n",
        "required_tweets = ['date','positive','neutral','negative','total']\n",
        "df_noOfTweets = df_noOfTweets[required_tweets]\n",
        "\n",
        "df_polarity_line = df_polarity_line.sort_values(by = 'date')\n",
        "df_polarity_line.reset_index(inplace=True)\n",
        "required_polarity = ['date','polarity']\n",
        "df_polarity_line = df_polarity_line[required_polarity]\n",
        "print(df_polarity_line)    "
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "          date    polarity\n",
            "0   2017-09-01   44.841279\n",
            "1   2017-09-02   34.723938\n",
            "2   2017-09-03   30.123810\n",
            "3   2017-09-04   43.906721\n",
            "4   2017-09-05   51.609893\n",
            "..         ...         ...\n",
            "86  2017-11-26  102.080079\n",
            "87  2017-11-27  135.689441\n",
            "88  2017-11-28  126.736091\n",
            "89  2017-11-29  171.530026\n",
            "90  2017-11-30  119.289935\n",
            "\n",
            "[91 rows x 2 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
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          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# print table of positive, negative, neutral tweets\n",
        "import plotly\n",
        "import plotly.figure_factory as ff\n",
        "\n",
        "table = ff.create_table(df_noOfTweets)\n",
        "plotly.offline.iplot(table, filename='df_noOfTweets')"
      ],
      "execution_count": 59,
      "outputs": [
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              "\n",
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              "        </div>\n",
              "</body>\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DtrIfyvqs91l",
        "outputId": "96ffeb84-2d7d-4e5b-f112-919d2374cd84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        }
      },
      "source": [
        "import plotly.graph_objs as go\n",
        "from plotly.offline import *\n",
        "\n",
        "trace1 = go.Scatter(\n",
        "    x=df_noOfTweets['date'],\n",
        "    y=df_noOfTweets['total'],\n",
        "    mode = 'lines',\n",
        "    name='No. Of Tweets'\n",
        ")\n",
        "\n",
        "#total number of tweets in dataset\n",
        "print(df_noOfTweets['total'].sum())\n",
        "\n",
        "trace2 = go.Scatter(\n",
        "    x=df_noOfTweets['date'],\n",
        "    y=df_noOfTweets['neutral'],\n",
        "    mode = 'lines',\n",
        "    name='Neutral'\n",
        ")\n",
        "trace3 = go.Scatter(\n",
        "    x=df_noOfTweets['date'],\n",
        "    y=df_noOfTweets['positive'],\n",
        "    mode = 'lines',\n",
        "    name='Positive'\n",
        ")\n",
        "trace4 = go.Scatter(\n",
        "    x=df_noOfTweets['date'],\n",
        "    y=df_noOfTweets['negative'],\n",
        "    mode = 'lines',\n",
        "    name='Negative'\n",
        ")\n",
        "data = [trace1, trace2, trace3, trace4]\n",
        "layout = go.Layout(\n",
        "    title='Number of Tweets'\n",
        ")\n",
        "fig = go.Figure(data=data, layout=layout)\n",
        "plotly.offline.iplot(fig, filename='No_Of_Tweets.html') # Plot No. of Tweets\n"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "54827\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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          "metadata": {
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KKek-J7Es91u",
        "outputId": "b7603d33-aaba-454e-9971-6e38d09208b1",
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      "source": [
        "# bitcoin_file = '/content/drive/My Drive/database/bitcoinHistoricalData/bitcoin.csv'\n",
        "data = pd.read_csv(bitcoin_file)\n",
        "req_features = ['Timestamp','Close']\n",
        "df_bitcoin = pd.DataFrame(columns=['date','close'])\n",
        "frequency=0\n",
        "sum=0\n",
        "cur_date=\"\"\n",
        "for index in data.index:\n",
        "  date = data['Timestamp'][index]\n",
        "  date = date.split(' ')[0]\n",
        "  value = float(data['Close'][index])\n",
        "  if date != cur_date:\n",
        "    if cur_date != \"\":\n",
        "      sum = sum/frequency\n",
        "      temp = pd.DataFrame({\"date\":[cur_date], \"close\":[sum]}) \n",
        "      df_bitcoin = df_bitcoin.append(temp, ignore_index = True)\n",
        "    cur_date = date\n",
        "    frequency = 1\n",
        "    sum = value\n",
        "  else:\n",
        "    sum += value\n",
        "    frequency += 1  \n",
        "sum = sum/frequency\n",
        "temp = pd.DataFrame({\"date\":[cur_date], \"close\":[sum]}) \n",
        "df_bitcoin = df_bitcoin.append(temp, ignore_index = True)\n",
        "# print(df_bitcoin)\n",
        "df_bitcoin = pd.merge(df_bitcoin, df_polarity_line,left_on='date', right_on='date')\n",
        "table = ff.create_table(df_bitcoin)\n",
        "plotly.offline.iplot(table, filename='df_bitcoin')\n",
        "# print(df_bitcoin.shape)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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    {
      "cell_type": "code",
      "metadata": {
        "id": "XgomsC1bs912",
        "outputId": "201e7775-720e-4d00-e7e5-a75751f771b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "source": [
        "# ============================================== #\n",
        "# Plot Polarity, Price in USD(Close) and Volume  #\n",
        "# ============================================== #\n",
        "trace1 = go.Scatter(\n",
        "    x=df_bitcoin['date'],\n",
        "    y=df_bitcoin['close'],\n",
        "    mode = 'lines',\n",
        "    name='Closing Price'\n",
        ")\n",
        "trace2 = go.Scatter(\n",
        "    x=df_bitcoin['date'],\n",
        "    y=df_bitcoin['polarity'],\n",
        "    mode = 'lines',\n",
        "    name='Polarity',\n",
        "    yaxis='y2'\n",
        ")\n",
        "data = [trace1, trace2]\n",
        "layout = go.Layout(\n",
        "    title='Sentiment Polarity Vs. Closing Price (in USD)',\n",
        "    yaxis=dict(\n",
        "        title='Closing Price.'\n",
        "    ),\n",
        "    yaxis2=dict(\n",
        "        title='Polarity',\n",
        "        titlefont=dict(\n",
        "            color='rgb(148, 103, 189)'\n",
        "        ),\n",
        "        tickfont=dict(\n",
        "            color='rgb(148, 103, 189)'\n",
        "        ),\n",
        "        overlaying='y',\n",
        "        side='right'\n",
        "    )\n",
        ")\n",
        "fig = go.Figure(data=data, layout=layout)\n",
        "\n",
        "plotly.offline.iplot(fig, filename='Sentiment_vs_Market.html')"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Sentiment Polarity Vs. Closing Price (in USD)\"}, \"yaxis\": {\"title\": {\"text\": \"Closing Price.\"}}, \"yaxis2\": {\"overlaying\": \"y\", \"side\": \"right\", \"tickfont\": {\"color\": \"rgb(148, 103, 189)\"}, \"title\": {\"font\": {\"color\": \"rgb(148, 103, 189)\"}, \"text\": \"Polarity\"}}},\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HlIUzbRKhbtK",
        "outputId": "2e6ce90c-6275-4146-f115-79cf453b3eb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "# Regression in polarity vs closing price\n",
        "import seaborn as sns\n",
        "import matplotlib as plt\n",
        "sns.regplot(x=df_bitcoin['polarity'], y=df_bitcoin['close'], color='red', marker='*')"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efc57c6dc88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oix1Zox_jff1",
        "outputId": "8567dde6-34a0-403c-89ca-ea11dd9bcedc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Calculating the Pearson coefficient\n",
        "from scipy import stats\n",
        "pearson_coef, p_value = stats.pearsonr(df_bitcoin['polarity'], df_bitcoin['close'])\n",
        "print(pearson_coef)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8375567867512373\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mTcKKQ_GdOtm",
        "outputId": "4815d880-fdb2-49ac-f2ed-35b219659583",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "# find skewness of tweets data\n",
        "skewValue = df_bitcoin.skew(axis=0)\n",
        "print(skewValue)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "close       0.795989\n",
            "polarity    1.638047\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}