sentiment-analysis-on-product-reviews

🛍️ Sentiment Analysis on Product Reviews

Week 1 Project | AI & Data Analyst Internship

An end-to-end Sentiment Analysis system that automatically reads e-commerce product reviews, classifies them as Positive, Negative, or Neutral, and generates meaningful business insights through data visualization.

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📌 Problem Statement

E-commerce platforms receive thousands of customer reviews every day. Manually reading each review to understand customer satisfaction is time-consuming and inefficient.

This project solves that by building an automated sentiment analysis pipeline that:

• Reads and cleans raw product review data
• Applies NLP-based sentiment scoring to every review
• Classifies each review into Positive / Negative / Neutral
• Presents findings through charts and a written summary report

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📁 Repository Structure

sentiment-analysis-on-product-reviews/
│
├── Sentiment_Analysis_on_Product_Reviews.ipynb   # Main Jupyter Notebook (all 5 tasks)
├── Dataset/                                       # Raw product reviews CSV dataset
├── Charts/                                        # All generated visualization charts
└── Summary.pdf                                    # Final insights & summary report

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✅ Tasks Completed

Task 1 — Data Loading & Exploration

• Loaded the CSV dataset using Pandas
• Displayed the first 10 rows to understand the structure
• Checked total number of rows and columns
• Identified the review text column for analysis

Task 2 — Data Cleaning

• Removed rows where review text was empty or null
• Removed duplicate reviews
• Kept only the essential columns: review text and rating/score

Task 3 — Sentiment Analysis

• Used the TextBlob library to calculate polarity score for each review
• Applied the following classification logic:
  • score > 0 → Positive
  • score < 0 → Negative
  • score = 0 → Neutral
• Added a new Sentiment label column to the dataset

Task 4 — Visualizations (3 Charts)

• Chart 1 — Bar Chart: Count of Positive, Negative, and Neutral reviews
• Chart 2 — Pie Chart: Percentage distribution of each sentiment category
• Chart 3 — Custom Chart: Creative visualization (e.g., rating vs sentiment comparison / most common words in negative reviews)

All charts are saved in the Charts/ folder.

Task 5 — Insights & Summary

Answered the following business questions inside the notebook:

• What percentage of reviews are positive?
• What do customers mostly complain about in negative reviews?
• What was surprising in the data?
• One actionable recommendation for the business

Full written summary available in Summary.pdf.

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🛠️ Tools & Libraries Used

Tool	Purpose
Python 3.x	Main programming language
Google Colab	Development environment
Pandas	Data loading and cleaning
TextBlob	Sentiment polarity analysis
Matplotlib / Seaborn	Charts and visualizations

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🚀 How to Run This Project

1. Open in Google Colab

Click the link below to open the notebook directly in Google Colab (no installation needed):

Or manually:

1. Go to colab.research.google.com
2. Click File → Open Notebook → GitHub
3. Paste this repo URL: https://github.com/smohan-21/sentiment-analysis-on-product-reviews
4. Select Sentiment_Analysis_on_Product_Reviews.ipynb 2. Install required libraries (run this cell first in Colab)

!pip install textblob

3. Download TextBlob language data

import nltk
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')

4. Upload the dataset

Upload the CSV file from the Dataset/ folder when prompted, or mount your Google Drive:

from google.colab import drive
drive.mount('/content/drive')

Run all cells from top to bottom to reproduce the full analysis.

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📊 Key Outputs

• Cleaned dataset with sentiment labels added
• 3 visualizations saved in the Charts/ folder
• Business insights written inside the notebook (Task 5)
• Summary report: Summary.pdf

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👤 Author

Mohan S AI & Data Analyst Intern GitHub: @smohan-21

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📄 License

This project is built for internship and educational purposes.