Overview:

The Sentiment Analysis for Social Media project is a Machine Learning and Natural Language Processing (NLP) based system that automatically analyzes user opinions, comments, and posts from platforms like Twitter, Instagram, Facebook, or YouTube to determine whether the expressed sentiment is positive, negative, or neutral.

The goal is to help organizations, brands, and individuals understand public perception, feedback, and emotional tone in real time. This system is widely used in digital marketing, brand reputation management, and political analysis.

Objectives:

• To analyze social media text and identify the emotional sentiment behind it.

• To help businesses and organizations track public opinion trends.

• To visualize sentiment insights using graphs and dashboards.

• To use NLP and ML algorithms for accurate sentiment classification.

Key Features:

1. Text Sentiment Classification: Categorizes posts/comments as Positive, Negative, or Neutral.

2. Social Media API Integration: Fetches real-time data from Twitter, Facebook, or YouTube APIs.

3. Keyword and Hashtag Analysis: Tracks trending keywords and topics.

4. Visualization Dashboard: Displays sentiment scores, pie charts, and trend graphs.

5. Dataset Training: Uses pre-labeled sentiment datasets to train ML models.

6. Preprocessing Module: Cleans text (removes emojis, stopwords, URLs, special characters).

7. Live Tweet/Comment Analysis: Users can enter any post or topic to analyze public mood.

8. Multilingual Support (optional): Detects sentiment in multiple languages.

9. Polarity Scoring: Assigns sentiment scores (e.g., +0.8 = positive, -0.6 = negative).

10. Admin Dashboard: View overall statistics and user interaction data.

Tech Stack:

• Frontend: HTML, CSS, Bootstrap, JavaScript

• Backend: Python (Flask/Django) / Node.js / PHP

• Database: MySQL / MongoDB

• Machine Learning & NLP:

  • Libraries: scikit-learn, pandas, NumPy, NLTK, TextBlob, spaCy, TensorFlow

  • Algorithms: Logistic Regression, Naïve Bayes, LSTM, or BERT

• APIs: Twitter API (Tweepy), YouTube Data API, Facebook Graph API

• Visualization: Matplotlib, Seaborn, Plotly

Workflow:

1. Data Collection:

   • The system fetches tweets or comments based on hashtags, keywords, or topics.

2. Data Preprocessing:

   • Cleans text (removes emojis, punctuation, links).

   • Converts words into vectors (TF-IDF or Word2Vec).

3. Model Training:

   • Train ML models using labeled datasets like Sentiment140 or IMDB Reviews.

4. Prediction:

   • Input new text → model predicts sentiment → assigns polarity score.

5. Visualization:

   • Dashboard displays sentiment summary in percentages and charts.