AmirhosseinHonardoust/Sentiment-Analysis-NLP
Customer reviews sentiment analysis with Python and NLP. Generates a synthetic dataset of positive, neutral, and negative reviews, applies preprocessing (tokenization, stopwords, lemmatization), and builds TF-IDF features. Trains classifiers (Naive Bayes, Logistic Regression, Random Forest) with evaluation, confusion matrix and top features.
This project helps customer experience managers, product marketers, or small business owners understand the sentiment behind customer feedback. It takes a collection of customer reviews and determines whether each review expresses positive, neutral, or negative sentiment. The output includes categorized reviews, performance reports, and visualizations like word clouds showing common terms for each sentiment.
No commits in the last 6 months.
Use this if you need to quickly categorize customer feedback into positive, neutral, or negative to gauge customer satisfaction or identify common issues from text reviews.
Not ideal if you need to analyze nuanced emotions beyond basic sentiment (e.g., anger, joy, surprise) or work with non-English reviews.
Stars
27
Forks
1
Language
Python
License
MIT
Category
Last pushed
Sep 11, 2025
Commits (30d)
0
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