Sentiment-Analysis-on-Womens-Clothing-E-Commerce-Reviews-Dataset

Sentiment analysis training data source

1. http://ptrckprry.com/course/ssd/data/positive-words.txt
2. http://ptrckprry.com/course/ssd/data/negative-words.txt

Review data source: Kaggle

Columns in the dataset:

Name	Meaning
Clothing ID	Unique ID of the product
Age	Age of the reviewer
Title	Title of the review
Review	Text review
Rating	Product rating by reviewer
Recommended IND	Whether the product is recommended or not by the reviewer
Positive Feedback Count	Number of positive feedback on the review
Division Name	Name of the division product is in
Department Name	Name of the department product is in
Class Name	Type of product

Objective:

To do exploratory sentiment analysis and tie it back to product types.

Findings:

Based on the analysis, it is clear that both the reviews for Tops and Dresses contain positive sentiment in abundance to negative sentiment. Anecdotal observation from the plots also suggests that reviews for Dresses tend to be more positive than reviews for Tops.