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A data science project that collects social media mentions of brands from Twitter and Reddit, performs sentiment analysis using NLP, and presents insights through an interactive dashboard. Brands can track public perception, identify trending complaints, and compare sentiment against competitors.
Problem Statement
Brands struggle to monitor and understand public sentiment across multiple social media platforms. Manual monitoring is impractical given the volume of posts. Existing tools are expensive and not customizable. An affordable, open-source sentiment analysis dashboard would help small businesses and marketing teams make data-driven decisions about their brand strategy.
Objectives
Build data collection pipelines for Twitter and Reddit APIs
Train a sentiment classification model with 85%+ accuracy
Create an interactive visualization dashboard
Implement trend detection and alert mechanisms
Design competitor comparison analytics
Modules
Data Collection
API integrations with Twitter and Reddit for real-time and historical data collection with keyword filtering.
Text Preprocessing
Clean, tokenize, and normalize social media text handling slang, emojis, and hashtags.
Sentiment Model
Fine-tuned BERT model for sentiment classification (positive, negative, neutral) on social media text.
Dashboard
Streamlit/Dash dashboard with sentiment trends, word clouds, top mentions, and geographic distribution.
Alert System
Detect sudden sentiment drops and send email/Slack alerts for potential PR crises.
Dataset
Twitter Sentiment140 dataset (1.6M tweets) for initial training. Live data collected via Twitter API v2 and Reddit PRAW.
Expected Output
An interactive dashboard showing real-time brand sentiment across social media platforms. Users can view sentiment trends over time, drill down into individual mentions, compare against competitors, see word clouds of common themes, and receive alerts when sentiment drops significantly.
Future Scope
Extend to more platforms (Instagram, YouTube comments, news articles), add aspect-based sentiment analysis (product quality vs customer service), implement influencer identification, and build a predictive model for sentiment forecasting.