This project aims to analyze social media data to determine public sentiment toward a brand or product. By applying text analytics and natural language processing, the project helps organizations understand customer opinions and improve brand reputation strategies.
Collect social media text data using APIs or open datasets.
Clean textual data by removing noise, emojis, and stopwords.
Perform tokenization, stemming, and lemmatization.
Conduct exploratory text analysis and word frequency analysis.
Implement sentiment classification using machine learning techniques.
Classify text into positive, negative, or neutral sentiments.
Evaluate model accuracy using confusion matrices.
Visualize sentiment distribution using charts and word clouds.
Interpret results to understand public perception.
Prepare documentation explaining data sources, models, and findings.
