Turning customer words into valuable insights — using NLP.
Project Overview
This project leverages Natural Language Processing (NLP) to perform sentiment analysis on AirPod reviews. We aim to classify the sentiment expressed in customer reviews into positive, negative, or neutral using the Naive Bayes classifier. This allows brands to understand feedback at scale and take strategic actions based on customer sentiment.
Dataset Links
Link to dataset: [Download from Kaggle]
Columns used : ‘Reviews’, ‘ID’, ‘Rating
Tools & Technologies Used
Methodology / Process
Data Loading and Cleaning (null values, punctuation, casing)
1. Data Cleaning (null values, punctuation, casing).
2. Text Preprocessing and cleaning.
3. Feature Extraction using TF-IDF
4. Model Building and Splitting data (Train/Test Split).
5. Sentiment Classification using different methods.
6. Evaluation using accuracy.
Text Cleaning
Few More Screenshots
Feature Extraction using TF-IDF
Model Building and Splitting data (Train/Test Split)
Logistics Regression
Naive Bayes
Randon Forest
Decision Tree Regression
Multidimonal Classification
Bilstm
Bert
Model Comparison and Key Findings
Saving Best Model
Outcome / Business Value
This project shows how sentiment analysis can:
– Help brands gauge customer satisfaction
– Identify pain points in product experience
– Improve product marketing based on feedback patterns
🔐 Full Project Files – Locked Content
This download contains:
✔️ Full Jupyter Notebook
✔️ Cleaned Review Dataset (.csv)
✔️ Trained Naive Bayes Model (.pkl)
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