Project Overview:

The Social Media Trend Analyzer is a web-based platform that collects, processes, and analyzes social media data (like tweets, posts, or hashtags) in real-time or periodically. It uses data science, natural language processing (NLP), and machine learning (ML) to detect emerging trends, analyze user sentiment, and visualize popular topics or hashtags over time.

This project is highly relevant in areas such as marketing, public opinion analysis, event monitoring, and crisis response.

Key Features:

1.  Real-time Hashtag & Keyword Tracking

2.  Data Visualization (Word Clouds, Graphs, Charts)

3.  Sentiment Analysis (Positive, Negative, Neutral)

4.  Geographic Trend Mapping (Optional)

5.  Time-Based Trend Analysis

6.  Popular Post & Influencer Identification

7.  Downloadable Trend Reports

Technology Stack:

Backend (choose one):

• PHP + MySQL

• Node.js + MongoDB

• Java (Spring Boot) + PostgreSQL

Frontend:

• HTML5, CSS3, Bootstrap

• JavaScript (Chart.js, WordCloud.js, D3.js)

• AJAX for live updates

Data Science/ML Engine (Python):

• Libraries: Tweepy, NLTK, TextBlob, Scikit-learn, SpaCy

• Models:

  • NLP for text cleaning & tokenization

  • Naive Bayes / Logistic Regression for sentiment analysis

  • TF-IDF or BERT for trend extraction

System Workflow:

1. Data Collection

   • Connect to Twitter API (Tweepy) or other sources

   • Stream/filter posts based on keywords or hashtags

   • Store structured data (text, timestamp, location, user info)

2. Data Processing (Python Backend)

   • Clean & preprocess text (remove stop words, symbols, links)

   • Run sentiment analysis and frequency analysis

   • Identify top hashtags, frequent words, and user mentions

3. Visualization (Web Dashboard)

   • Live charts for most used hashtags

   • Word cloud for top terms

   • Sentiment pie chart (positive/negative/neutral)

   • Timeline graph for trend evolution