This document aims to classify Amazon product reviews using sentiment analysis. It introduces text classification and describes the dataset, data processing steps, and classification system model used, which includes feature extraction, selection, and classification algorithms like KNN, Random Forest, and SVM. The document also discusses evaluation metrics, applications, results, and concludes that the system can help retailers analyze customer sentiment to improve their products.

1. Sentiment analysis to
classify Amazon Product
Reviews
Gaurav Kumar
Abhishek Kumar
Washim khan

2. Table of content
 Aim & Objective
 Introduction and text classification
 Dataset description
 Data Processing
 System Model
 Algorithms
 Result & analysis
 Future work
 conclusion

3. Aim & Objective
Classify Amazon Product Reviews

4. Labelled vs Unlabelled data

5. Why labelled data is important?
 Model Success
 Model Performance

6. Text Classification
“This is a great
camera. Easy to
use and take
great pictures”
Text processing
Feature
Extractor "3-Positive"

7. Data Processing Steps
 We have computed the co-relation
between each and other attributes
 The attributes are not highly co-related
 We just chose the review text to
generate features
 Overall is the class label(1-5)

8. Classification System Model
Stage - 1
Stage - 2
• Training a model
• Evaluating Performance by predicting label
for known data point
• Model will predict the class label for data
point of unknown label

9. Training Stage
Data
Processing
Feature
Extraction
Feature
Selection
Classification
Algorithm
Data
Processing
Feature
Extraction
Prediction
Model
Dataset
Labelled
data
Pre-Processed
Data
High dimensional
Feature Vectors
Low dimensional
Feature Vectors
Prediction Stage
New
Review
Un-Labelled
Data
High dimensional
Feature Vectors
Low dimensional
Feature Vectors

10. Algorithms
 K-Nearest Neighbors
 Random Forest
 SVM
 Decision Trees

11. Evaluation Metrics

12. Applications
 Retailers who are new to the business
 Recommended systems
 Advertising brands
 Competitor monitoring

13. Result

14. Future Work
 Challenges: Detecting Unfair Reviews
 The model is getting trained by the reviews which are
positive but given a less rating
 The performance of the model can be improved by
detecting and eliminating those from the training data
 The time taking by our model can be improved

15. Conclusion
 The application provides the retailer the rating of his/her product.
 This also provides an oversight of how many users were interested in
the product, which could be biased using the number of reviewers in
the database.
 The interestingness with respect to the product can also be measured
with the number of positive, neutral, and negative reviews obtained.
 This analysis could help the retailers make subtle changes either to
the product service or with how the product looks according to the
number of users.