Sentiment Analysis of Product Reviews

    Analyzed product reviews to classify sentiment and visualize trends using NLP techniques.

    Muhammad Zeeshan

    Technologies Used

    Python
    NLTK
    VADER
    TextBlob
    Matplotlib
    Seaborn
    Pandas

    Key Features

    1

    Multi-class sentiment classification (positive, negative, neutral)

    2

    Aspect-based sentiment analysis

    3

    Trend visualization over time

    4

    Word cloud generation for key themes

    5

    Comparative analysis across products

    Implementation

    Implemented a comprehensive sentiment analysis pipeline using multiple NLP techniques including VADER, TextBlob, and custom LSTM models. Performed aspect extraction and sentiment scoring for each product feature.

    Results & Impact

    Analyzed 50,000+ product reviews with 88% accuracy. Identified key pain points and positive features, leading to actionable product improvement insights.