Sentiment Analysis Tool
machine-learning
nlp
api

Sentiment Analysis Tool

Develop a sophisticated machine learning-powered application that analyzes text sentiment using natural language processing. Ideal for developers interested in practical AI applications and text analysis.

Time Breakdown

Planning: ~2 hours
Coding: ~5 hours
Testing: ~3 hours

Difficulty: Advanced Challenge

Sentiment Analysis Tool

Project Overview

Build a powerful sentiment analysis tool that leverages machine learning to analyze text sentiment. This project combines NLP techniques with modern web technologies to create a practical AI application.

User Stories

  1. As a data analyst, I want to:

    • Analyze text sentiment in real-time
    • Process large batches of text data
    • Export analysis results in various formats
    • Track sentiment trends over time

  2. As a developer, I want to:

    • Access sentiment analysis via API
    • Customize the model parameters
    • Monitor model performance metrics
    • Handle multiple languages efficiently

  3. As a business user, I want to:

    • Upload documents for sentiment analysis
    • Generate comprehensive reports
    • Visualize sentiment distributions
    • Share analysis results with team members

Example Code

from transformers import pipeline
from flask import Flask, request, jsonify

app = Flask(__name__)
sentiment_analyzer = pipeline("sentiment-analysis")

@app.route('/analyze', methods=['POST'])
def analyze_sentiment():
    text = request.json.get('text')
    result = sentiment_analyzer(text)[0]

    return jsonify({
        'text': text,
        'sentiment': result['label'],
        'confidence': result['score']
    })

Related Projects

Real-time Chat Application

intermediate

Create a feature-rich chat platform that rivals modern messaging apps. This project combines real-time communication, rich media sharing, and seamless user experience to build a professional chat solution.

6-8 hours

Learning Outcomes

  • Understanding NLP fundamentals and applications
  • Training and evaluating ML models
  • Building scalable RESTful APIs
  • Implementing data preprocessing pipelines
  • Deploying machine learning models

Project Requirements

Progress Tracker

0 of 8 completed
  • Train a sentiment analysis model
  • Create an API endpoint for predictions
  • Handle text preprocessing
  • Implement model evaluation metrics
  • Add support for multiple languages
  • Create a simple web interface
  • Add batch processing support
  • Export results to CSV/JSON

Share Project