metadata

title: News Sentiment Analyzer
emoji: 🐨
colorFrom: gray
colorTo: blue
sdk: streamlit
sdk_version: 1.43.2
app_file: app.py
pinned: false
short_description: 'simple app to analyze news sentiment '

News Summarization & Sentiment Analysis

Overview

This project is a web-based application that extracts news articles related to a given company, performs sentiment analysis, generates a comparative analysis, and converts the summarized content into Hindi speech.

Features

News Extraction: Fetches news articles using NewsAPI and scrapes additional content when available.
Sentiment Analysis: Categorizes each article as Positive, Negative, or Neutral.
Comparative Analysis: Provides an overview of sentiment distribution across articles.
Text-to-Speech: Converts summarized news titles into Hindi speech.
Web Interface: Allows users to input a company name via a Streamlit UI.
API Integration: Backend developed using FastAPI to communicate with the frontend.

Installation

Prerequisites

Ensure you have Python installed (>= 3.8). Install the required dependencies:

pip install requests nltk beautifulsoup4 gtts deep-translator streamlit fastapi pydantic uvicorn

Download NLTK Data

To enable sentiment analysis, run:

import nltk
nltk.download('vader_lexicon')

Usage

Running the FastAPI Backend

Start the FastAPI server using:

uvicorn news:app --reload

Running the Streamlit UI

Launch the web application:

streamlit run news.py

API Endpoints

POST /get_news/

Request Body:
```
{ "company": "Tesla" }
```

Response:

{
  "articles": [...],
  "sentiment_summary": {"Positive": 3, "Negative": 4, "Neutral": 3}
}

Deployment

Deploy FastAPI Backend: Use services like AWS, Heroku, or Render.
Deploy Streamlit UI: Host on Streamlit Cloud or Hugging Face Spaces.

Acknowledgments

NewsAPI for fetching news.
NLTK for sentiment analysis.
Google Translate API for Hindi translation.
Streamlit for the UI.
FastAPI for backend API.