To build a sentiment analysis system that evaluates customer opinions about a brand from social media platforms. The project aims to measure brand perception, detect public sentiment trends, and provide actionable insights for improving brand positioning and marketing strategies.
Collect social media data using APIs or sample datasets.
Preprocess text data (tokenization, stop-word removal, stemming).
Perform sentiment classification using NLP libraries (NLTK, TextBlob, or Transformers).
Categorize feedback into positive, negative, and neutral sentiments.
Analyze trends over time and identify common customer concerns.
Build visualization dashboards showing sentiment distribution.
Compare competitor brand sentiment performance.
Implement word cloud visualization for frequently used keywords.
Evaluate model accuracy using confusion matrix and performance metrics.
Identify factors affecting brand reputation.
Provide recommendations for brand improvement strategies.
Prepare a market positioning report based on findings.
