Sentiment Analysis on Product Reviews

Built a sentiment analysis model to classify product reviews into positive, neutral, or negative categories. This helps businesses better understand customer feedback and improve their products and services.

Technologies Used:

Python (Pandas, NumPy, Scikit-learn)

NLP Libraries: NLTK / spaCy / TextBlob

Data preprocessing (cleaning, tokenization, stopword removal)

Feature extraction using TF-IDF and Word Embeddings

Classification algorithms: Logistic Regression, SVM, or BERT

Evaluation metrics: Accuracy, Precision, Recall, F1-score

Achievements:

Achieved high accuracy in sentiment prediction

Enabled automatic classification of thousands of reviews

Helped identify common pain points and product strengths