label_tweet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Negative Comments Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     /home/ec2-user/nltk_data...\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import re\n",
    "import nltk\n",
    "nltk.download('vader_lexicon')\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = pd.read_csv(\"G:/Shared drives/Unidad Compartida Pachitos/Data Science Projects Pachitos/HackOff-CompanySentiments/tesla_sf.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker import get_execution_role\n",
    "\n",
    "role = get_execution_role()\n",
    "data_location = 's3://tweets-hackoff1/tweets_tesla_sf.csv'\n",
    "df = pd.read_csv(data_location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SentimentAnalysisTweets():\n",
    "\n",
    "    def __init__(self, df, column_name):\n",
    "        self.tweets_ = df\n",
    "        self.sid_ = SentimentIntensityAnalyzer()\n",
    "\n",
    "        self.preprocess(column_name)\n",
    "\n",
    "    def preprocess(self, column_name):\n",
    "        self.tweets_['tweets_clean'] = self.tweets_.apply(lambda row: self.clean_text(row[column_name]), axis=1)\n",
    "\n",
    "        return self\n",
    "\n",
    "    def clean_text(self, observation):\n",
    "        \n",
    "        observation = re.sub(r'[^\\$\\w\\s]', '', str(observation).lower().strip())\n",
    "        observation = re.sub(r'^RT[\\s]+', '', observation)\n",
    "        observation = re.sub(r'https?:\\/\\/.*[\\r\\n]*', '', observation) # removing hyperlinks\n",
    "        observation = re.sub(r'#', '', observation) #removing hash # sign\n",
    "\n",
    "        return observation\n",
    "\n",
    "    def create_label(self):\n",
    "        self.tweets_['sentiment'] = self.tweets_.apply(lambda row: self.sid_.polarity_scores(row['text']), axis=1)\n",
    "        self.tweets_['flag'] = self.tweets_['sentiment'].apply(lambda row: 'positive' if row.get('compound') >= 0 else 'negative')\n",
    "\n",
    "        return self"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweet_class = SentimentAnalysisTweets(df, column_name='text')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.SentimentAnalysisTweets at 0x7f89d4988860>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_class.create_label()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_label = tweet_class.tweets_.copy()\n",
    "df_label.drop(['tweets_clean','sentiment'], axis=1, inplace=True)\n",
    "df_label.to_csv('label_tweets.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate S3 client and upload to s3\n",
    "import boto3\n",
    "\n",
    "s3 = boto3.resource('s3')\n",
    "s3.meta.client.upload_file('label_tweets.csv', 'tweets-hackoff1', 'label_tweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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