Project_tweet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets = [\n",
    "    \"Wow, what a great day today!! #sunshine\",\n",
    "    \"I feel sad about the things going on around us. #covid19\",\n",
    "    \"I'm really excited to learn Python with @JovianML #zerotopandas\",\n",
    "    \"This is a really nice song. #linkinpark\",\n",
    "    \"The python programming language is useful for data science\",\n",
    "    \"Why do bad things happen to me?\",\n",
    "    \"Apple announces the release of the new iPhone 12. Fans are excited.\",\n",
    "    \"Spent my day with family!! #happy\",\n",
    "    \"Check out my blog post on common string operations in Python. #zerotopandas\",\n",
    "    \"Freecodecamp has great coding tutorials. #skillup\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_tweets = len(tweets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "happy_words = ['great', 'excited', 'happy', 'nice', 'wonderful', 'amazing', 'good', 'best']\n",
    "sad_words = ['sad', 'bad', 'tragic', 'unhappy', 'worst']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "count =0\n",
    "for tweet in tweets:\n",
    "   for word in happy_words:\n",
    "      if word in tweet:\n",
    "         count =  count +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "happy_fraction = count/number_of_tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happy_fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "sad_fraction = 1-happy_fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is the sad fraction value of TWEET:- 0.4\n"
     ]
    }
   ],
   "source": [
    "print(\"here is the sad fraction value of TWEET:- {}\".format(sad_fraction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiment_answer = happy_fraction - sad_fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19999999999999996"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment_answer"
   ]
  }
 ],
 "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": "3.9.13"
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 },
 "nbformat": 4,
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}