simple sentiment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# https://www.youtube.com/watch?v=uPKnSq6TaAk\n",
    "# https://www.youtube.com/watch?v=Lu1nskBkPJU&list=PL7Lkk4UtXtOw04G1nRapMNgd2myNJCZSJ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
    "from scipy.special import softmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiment1 = \"@elonmusk your soo cool @ twitter 😀 https://twitter.com/elonmusk\"\n",
    "sentiment2 = 'Worst content I have ever seen! Unsubscribed 😒😒'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# precprcess tweet\n",
    "def preprocess_sentiment(tweet):\n",
    "    tweet_words = []\n",
    "    for word in tweet.split(' '):\n",
    "        if word.startswith('@') and len(word) > 1:\n",
    "            word = '@user'\n",
    "        \n",
    "        elif word.startswith('http'):\n",
    "            word = \"http\"\n",
    "        tweet_words.append(word)\n",
    "        tweet_proc = \" \".join(tweet_words)\n",
    "    return tweet_proc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweet_proc1 = preprocess_sentiment(sentiment1)\n",
    "tweet_proc2 = preprocess_sentiment(sentiment2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load model and tokenizer\n",
    "roberta = \"cardiffnlp/twitter-roberta-base-sentiment\"\n",
    "\n",
    "model = AutoModelForSequenceClassification.from_pretrained(roberta)\n",
    "tokenizer = AutoTokenizer.from_pretrained(roberta)\n",
    "\n",
    "labels = ['Negative', 'Neutral', 'Positive']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sentiment analysis\n",
    "encoded_tweet1 = tokenizer(tweet_proc1, return_tensors='pt')\n",
    "output1 = model(**encoded_tweet1)\n",
    "scores1 = output1[0][0].detach().numpy()\n",
    "scores1 = softmax(scores1)\n",
    "\n",
    "encoded_tweet2 = tokenizer(tweet_proc2, return_tensors='pt')\n",
    "output2 = model(**encoded_tweet2)\n",
    "scores2 = output2[0][0].detach().numpy()\n",
    "scores2 = softmax(scores2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentiment: @elonmusk your soo cool @ twitter 😀 https://twitter.com/elonmusk\n",
      "\n",
      "Sentiment Analysis\n",
      "Negative 0.0010310933\n",
      "Neutral 0.010395749\n",
      "Positive 0.9885732\n"
     ]
    }
   ],
   "source": [
    "print(\"Sentiment:\",sentiment1)\n",
    "print(\"\\nSentiment Analysis\")\n",
    "for i in range(len(scores1)):\n",
    "    l = labels[i]\n",
    "    s = scores1[i]\n",
    "    print(l,s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentiment: Worst content I have ever seen! Unsubscribed 😒😒\n",
      "\n",
      "Sentiment Analysis\n",
      "Negative 0.9717405\n",
      "Neutral 0.024161281\n",
      "Positive 0.0040982515\n"
     ]
    }
   ],
   "source": [
    "print(\"Sentiment:\",sentiment2)\n",
    "print(\"\\nSentiment Analysis\")\n",
    "for i in range(len(scores1)):\n",
    "    l = labels[i]\n",
    "    s = scores2[i]\n",
    "    print(l,s)"
   ]
  }
 ],
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