Compare_Sentiment_With_Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "53b2ea1a-1a15-430b-9dee-76c5966df672",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use random forest regression to predict crypto price behavior based on Tweet sentiment (with added text preprocessing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b6e81e72-7151-4bb1-ad51-0e9bdca3b193",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "import snscrape.modules.twitter as sntwitter\n",
    "import pandas as pd\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "import re\n",
    "from textblob import TextBlob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import requests\n",
    "from datetime import datetime, timedelta\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import SnowballStemmer, WordNetLemmatizer\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "from textblob import TextBlob\n",
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2785e8af-4aa6-4efa-926e-6f2a87665a40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initializing preprocessing variables\n",
    "stop_words = stopwords.words(\"english\")\n",
    "stemmer = SnowballStemmer(\"english\", ignore_stopwords=True)\n",
    "lemmatizer = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "da6c4727-0624-4f62-9b43-dba563a9e89f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a function to remove emojis\n",
    "def no_emojis(tweet):\n",
    "    regrex_pattern = re.compile(pattern = \"[\"\n",
    "        u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
    "        u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
    "        u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
    "        u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
    "                           \"]+\", flags = re.UNICODE)\n",
    "    return regrex_pattern.sub(r' ',tweet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c147bb84-4d96-4029-a0cc-6d3cebd0a355",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a function to clean the tweets\n",
    "def clean_tweet(tweet):\n",
    "    tweet = tweet.lower() #removes capitalization\n",
    "    tweet = re.sub('#ethereum', 'ethereum', tweet) #changes #ethereum to ethereum\n",
    "    tweet = re.sub('#[A-Za-z0-9]+', '', tweet) #removes any other string that comes after a hashtag\n",
    "    tweet = re.sub('\\\\n', '', tweet) #removes the newline character\n",
    "    tweet = re.sub('https?:\\/\\/\\S+', '', tweet) #removes hyperlinks\n",
    "    tweet = re.sub('@[^\\s]+','',tweet) #removes usernames\n",
    "    tweet = no_emojis(tweet) #removes emojis\n",
    "    tweet = word_tokenize(tweet) #tokenizes the tweets\n",
    "    tweet = [lemmatizer.lemmatize(t) for t in tweet if t not in stop_words] #lemmatizes the tweets\n",
    "    tweet = ' '.join([stemmer.stem(i) for i in tweet]) #stems the tweets and joins them in a string\n",
    "    return tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29336972-a5c5-4872-8290-8c751e92a13c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a function to get the text sentiment based on polarity\n",
    "def get_sentiment(score):\n",
    "    if score < 0:\n",
    "        return 'Negative'\n",
    "    elif score == 0:\n",
    "        return 'Neutral'\n",
    "    else:\n",
    "        return 'Positive'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7975d7d3-6a9c-4a6e-a364-0234de1e16d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating list to append tweet data to\n",
    "tweets = []\n",
    "def run_the_tweets(tweet):\n",
    "    cleaned_tweets = clean_tweet(tweet.content)\n",
    "    tweet_subjectivity = TextBlob(cleaned_tweets).sentiment.subjectivity\n",
    "    tweet_polarity = TextBlob(cleaned_tweets).sentiment.polarity\n",
    "    sentiment_score = get_sentiment(tweet_polarity)\n",
    "    tweets.append([tweet.content, tweet.date, cleaned_tweets, sentiment_score, tweet_subjectivity, tweet_polarity])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "824dea6f-f2bf-4719-93a7-b9abec4a1719",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using TwitterSearchScraper to scrape data and append tweets to list\n",
    "today = datetime.now() + timedelta(hours=5) # add 5 hours to today to match UTC time zone\n",
    "first_date = today - timedelta(days=30)\n",
    "first_date = first_date.strftime('%Y-%m-%d')\n",
    "second_date = today - timedelta(days=29)\n",
    "second_date = second_date.strftime('%Y-%m-%d')\n",
    "for x in range(31):\n",
    "    for i,tweet in enumerate(sntwitter.TwitterSearchScraper('ethereum exclude:retweets lang:en since:'+first_date+' until:'+second_date).get_items()):\n",
    "        if i>10:\n",
    "            break\n",
    "        run_the_tweets(tweet)\n",
    "    first_date = datetime.strptime(first_date, '%Y-%m-%d') + timedelta(days=1)\n",
    "    first_date = first_date.strftime('%Y-%m-%d')\n",
    "    second_date = datetime.strptime(second_date, '%Y-%m-%d') + timedelta(days=1)\n",
    "    second_date = second_date.strftime('%Y-%m-%d')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e188361-ab9f-4365-bdc1-0adf7289d8fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a data frame from the tweets list above\n",
    "tweets = pd.DataFrame(tweets, columns=['Text', 'Date', 'Cleaned Tweets', 'Sentiment', 'Subjectivity', 'Polarity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "268ef57d-ffa2-41d8-924f-4c62fb3fd499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Text</th>\n",
       "      <th>Date</th>\n",
       "      <th>Cleaned Tweets</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Subjectivity</th>\n",
       "      <th>Polarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@Afanetty1 Take a look at @BlockbotsNFT... onl...</td>\n",
       "      <td>2021-11-03 23:59:46+00:00</td>\n",
       "      <td>take look total 2969 genesi exist .. check ama...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1: Bitcoin price is $62963.32 (0.12% 1h)\\n2: E...</td>\n",
       "      <td>2021-11-03 23:59:21+00:00</td>\n",
       "      <td>1 : bitcoin price $ 62963.32 ( 0.12 % 1h ) 2 :...</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ethereum Miner Revenue Outpaces Bitcoin In 202...</td>\n",
       "      <td>2021-11-03 23:59:14+00:00</td>\n",
       "      <td>ethereum miner revenu outpac bitcoin 2021 ethe...</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ethereum Clears New All-Time High As Market Re...</td>\n",
       "      <td>2021-11-03 23:59:12+00:00</td>\n",
       "      <td>ethereum clear new all-tim high market remain ...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.459293</td>\n",
       "      <td>0.132121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DesertAnt found #ethereum in a User vault at t...</td>\n",
       "      <td>2021-11-03 23:59:06+00:00</td>\n",
       "      <td>desert found ethereum user vault locat ! join ...</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Text  \\\n",
       "0  @Afanetty1 Take a look at @BlockbotsNFT... onl...   \n",
       "1  1: Bitcoin price is $62963.32 (0.12% 1h)\\n2: E...   \n",
       "2  Ethereum Miner Revenue Outpaces Bitcoin In 202...   \n",
       "3  Ethereum Clears New All-Time High As Market Re...   \n",
       "4  DesertAnt found #ethereum in a User vault at t...   \n",
       "\n",
       "                       Date  \\\n",
       "0 2021-11-03 23:59:46+00:00   \n",
       "1 2021-11-03 23:59:21+00:00   \n",
       "2 2021-11-03 23:59:14+00:00   \n",
       "3 2021-11-03 23:59:12+00:00   \n",
       "4 2021-11-03 23:59:06+00:00   \n",
       "\n",
       "                                      Cleaned Tweets Sentiment  Subjectivity  \\\n",
       "0  take look total 2969 genesi exist .. check ama...  Positive      0.633333   \n",
       "1  1 : bitcoin price $ 62963.32 ( 0.12 % 1h ) 2 :...   Neutral      0.000000   \n",
       "2  ethereum miner revenu outpac bitcoin 2021 ethe...   Neutral      0.000000   \n",
       "3  ethereum clear new all-tim high market remain ...  Positive      0.459293   \n",
       "4  desert found ethereum user vault locat ! join ...   Neutral      0.000000   \n",
       "\n",
       "   Polarity  \n",
       "0  0.100000  \n",
       "1  0.000000  \n",
       "2  0.000000  \n",
       "3  0.132121  \n",
       "4  0.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at data frame\n",
    "tweets.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "87253b6a-f128-4dd4-9046-435bc06f8ddc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graphing polarity of tweets over 30 days\n",
    "sentiment_graph = tweets.plot.line(x='Date', y='Polarity', color='pink')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "27a9379b-acca-4fbd-a105-5db5e07aea04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new data frame with only polarity and grouping the remaining number values\n",
    "polarity = tweets.drop('Subjectivity', 1)\n",
    "sentiment = polarity.set_index('Date').groupby(pd.Grouper(freq='d')).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c83deb04-cdd6-4995-a72d-214b5fac7609",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calling the Crypto Compare API to return Ethereum prices for the last 30 days\n",
    "def get_crypto_price(symbol, exchange, day):\n",
    "    api_key = 'eeda2dcaa0edbb8bd14f77f682c79e556d34af4e17099c41261c49bc125c9c3b'\n",
    "    api_url = f'https://min-api.cryptocompare.com/data/v2/histoday?fsym={symbol}&tsym={exchange}&limit={day}&api_key={api_key}'\n",
    "    raw = requests.get(api_url).json()\n",
    "    df = pd.DataFrame(raw['Data']['Data'])[['time', 'close']].set_index('time')\n",
    "    df.index = pd.to_datetime(df.index, unit = 's')\n",
    "    return df\n",
    "#create a data frame with the prices\n",
    "eth = get_crypto_price('ETH', 'USD', 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "70774e16-a8a9-4424-a662-867289375c76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graph the prices\n",
    "line_graph = eth.plot.line(color='pink')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e948d73d-c1e2-4cf6-a982-652965f3edcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating new Ethereum price dataframe with the correct timezone\n",
    "eth1 = eth.copy()\n",
    "eth1.index = eth.index.tz_localize('UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aa86fd60-58db-4bac-a6ee-c5252afd7f37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an empty data frame with a list of dates as index starting on the current date \n",
    "today = datetime.now() + timedelta(hours=5) #adding 5 hours to match UTC time zone\n",
    "month_ago = today - timedelta(days = 30)\n",
    "today = today.strftime('%Y-%m-%d')\n",
    "month_ago = month_ago.strftime('%Y-%m-%d')\n",
    "df = pd.date_range(month_ago, today)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "42590060-7d3c-48d4-8eea-a9f0d500c9c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# combining dataframe values\n",
    "combined_df = pd.DataFrame({'Date': df[0], 'Polarity' : sentiment['Polarity'], 'Close Price' : eth1['close']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5c797132-9891-44dc-86db-6f61cd732506",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting up a random forest regression model\n",
    "x = combined_df.iloc[:, 1:2].values \n",
    "y = combined_df.iloc[:, 2].values\n",
    "regressor = RandomForestRegressor(n_estimators = 200, random_state = 0)\n",
    "regressor.fit(x, y)  \n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)\n",
    "Y_pred1 =regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1804dee6-9084-4864-ae7d-d595ec91a4f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the preojected and real data for the regression model\n",
    "Y_pred = regressor.predict(np.array([6.5]).reshape(1, 1))\n",
    "X_grid = np.arange(min(x), max(x), 0.01)             \n",
    "X_grid = X_grid.reshape((len(X_grid), 1))\n",
    "plt.scatter(x, y, color = 'pink')  \n",
    "plt.plot(X_grid, regressor.predict(X_grid), \n",
    "         color = 'purple') \n",
    "plt.title('Random Forest Regression')\n",
    "plt.xlabel('Polarity')\n",
    "plt.ylabel('Coin Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "70d85068-d70a-4980-a4f2-55d070dabe90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 43.668342857139706\n",
      "Mean Squared Error: 3369.1587471684784\n",
      "Root Mean Squared Error: 58.04445492179661\n",
      "Error rate: 1.3191921573135594 %\n"
     ]
    }
   ],
   "source": [
    "#printing error data\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, Y_pred1))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, Y_pred1))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, Y_pred1)))\n",
    "print('Error rate:', ((np.sqrt(metrics.mean_squared_error(y_test, Y_pred1)) / (4400)) * 100), '%')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}