DATA PRE-PROCESSING FOR NATURAL LANGUAGE PROCESSING

About Dataset

Women’s E-Commerce Clothing Reviews on Kaggle

Link Dataset:

https://www.kaggle.com/nicapotato/womens-ecommerce-clothing-reviews

Context

Welcome. This is a Women’s Clothing E-Commerce dataset revolving around the reviews written by customers. Its nine supportive features offer a great environment to parse out the text through its multiple dimensions. Because this is real commercial data, it has been anonymized, and references to the company in the review text and body have been replaced with “retailer”.

Content

This dataset includes 23486 rows and 10 feature variables. Each row corresponds to a customer review, and includes the variables:

• Clothing ID: Integer Categorical variable that refers to the specific piece
  being reviewed.
• Age: Positive Integer variable of the reviewers age.
• Title: String variable for the title of the review.
• Review Text: String variable for the review body.
• Rating: Positive Ordinal Integer variable for the product score granted by the customer from 1 Worst, to 5 Best.
• Recommended IND: Binary variable stating where the customer recommends the product where 1 is recommended, 0 is not recommended.
• Positive Feedback Count: Positive Integer documenting the number of other customers who found this review positive.
• Division Name: Categorical name of the product high level division.
• Department Name: Categorical name of the product department name.
• Class Name: Categorical name of the product class name.

Acknowledgements

Anonymous but real source

Inspiration

Nicapotato, an owner of dataset Women’s E-Commerce Clothing Reviews that looks forward to coming quality NLP! There are also some great opportunities for feature engineering and multivariate analysis.

Publication

Statistical Analysis on E-Commerce Reviews, with Sentiment Classification using Bidirectional Recurrent Neural Network

by Abien Fred Agarap - Github

Metadata

Usage Information

• License CC0: Public Domain
• Visibility public

Maintainers

• Dataset owner nicapotato

Updates

Expected update frequency (Not specified)
Last updated 2018-02-04
Date created 2018-02-04      
Current version Version 1

Objectives

Problem Framing

• How to cleaning text data from sentences?

Ideal Outcome

• A success metric is that sentences could be clear to use.
• Success means text data excellent for predicting sentiment analysis.
• Failure means text data used for sentiment predicted is no better than current heuristics.

Heuristics

• Consider raw data noisy, their data null, and ext. Assume that data used to make statistical analysis.

Formulation of the problem

• Text Cleaning

  Turn into lower case text
  Apply tokenize to each row
  Remove stopwords

• Prepocessing for Sentiment Analysis

  Applying Model, Variable Creation
  Converting 0 to 1 Decimal Score to a Categorical Variable

Results

The number of columns from this datasets.

Index(['Unnamed: 0', 'Clothing ID', 'Age', 'Title', 'Review Text', 'Rating',
       'Recommended IND', 'Positive Feedback Count', 'Division Name',
       'Department Name', 'Class Name', 'tokenized', 'Polarity Score',
       'Neutral Score', 'Negative Score', 'Positive Score', 'Sentiment',
       'tokenized_unlist', 'label'],
       dtype='object')

The total row from these datasets is 23486 rows and 19 feature variables.