I built this project to explore how Natural Language Processing can help brands understand customer opinions more effectively.
I collected product reviews from platforms like Amazon, Flipkart, and Google Play, and processed them through a Python-based NLP pipeline to identify sentiment trends and summarize user feedback.
The process began with data preprocessing — cleaning text, removing noise, and preparing it for analysis using libraries like NLTK, Pandas, and Regex.
I then used a BERT-based sentiment classification model to categorize reviews into positive, negative, and neutral sentiments.
To make the output more insightful, I implemented a text summarization component that automatically condensed long reviews into concise summaries using transformer-based algorithms.
Finally, I built a Streamlit dashboard to visualize the results interactively — showing sentiment distributions, keyword clouds, and top recurring themes extracted from reviews.
What made this project truly engaging was how it combined technical depth with user-centered design — turning abstract AI output into a tool that helps teams understand customer emotions at scale.
Tech Stack
- Languages: Python
- Libraries: NLTK, BERT, Transformers, Pandas, Matplotlib, Seaborn
- Frameworks & Tools: Streamlit, Jupyter Notebook
- Techniques: Sentiment Analysis, Text Summarization, Data Visualization
Key Learnings
- Implementing BERT-based NLP pipelines for sentiment classification
- Building end-to-end text analytics workflows from data cleaning to visualization
- Simplifying unstructured text into digestible business insights
- Designing intuitive dashboards to visualize AI results interactively
