sentiment_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Sentiment Analysis Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiment_analysis = pipeline(\"sentiment-analysis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_text = \"I enjoy studying computational algorithms.\"\n",
    "neg_text = \"I dislike sleeping late everyday.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label: POSITIVE\n",
      "Confidence Score: 0.9980718493461609\n",
      "\n",
      "Label: NEGATIVE\n",
      "Confidence Score: 0.9881595969200134\n"
     ]
    }
   ],
   "source": [
    "result = sentiment_analysis(pos_text)[0]\n",
    "print(\"Label:\", result['label'])\n",
    "print(\"Confidence Score:\", result['score'])\n",
    "print()\n",
    "\n",
    "result = sentiment_analysis(neg_text)[0]\n",
    "print(\"Label:\", result['label'])\n",
    "print(\"Confidence Score:\", result['score'])"
   ]
  }
 ],
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