Ecommerce product reviews - Pairwise ranking and sentiment analysis

Ecommerce product reviews - Pairwise ranking and sentiment analysis

This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.

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Prasanna Lakshmi T

Advisory System Analyst at IBM

Initially, I was unaware of how this would cater to my career needs. But when I stumbled through the reviews given on the website. I went through many of them and found them all positive. I would... Read More

Ray Han

Tech Leader | Stanford / Yale University

I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More

What will you learn

Understanding the problem statement and literature survey for review ranking
EDA over textual data
Reviews Text Data Preprocessing - Language Detection, Gibberish Detection, Profanity Detection, and Spelling Correction
How to find gibberish by Markov Chain Concept
Featuring Engineering: Extracting relevance from Reviews Data
Sentiment Analysis: Finding Polarity and Subjectivity from Reviews
Finding text content richness by TF-IDF
EDA with extracted Featured with Target Class
What is Learning to Rank
Pairwise Ranking: In-depth explained, how we used it to rank reviews
Converting Ranking problem to a Classification Problem
Classification Models Spot Checking
Pairwise Ranking reviews with Random Forest Classifier
Evaluation Metrics: Classification Accuracy and Ranking Accuracy
Saving the trained model and developing a Model-Data Pipeline for production use

Project Description

E-Commerce applications provide an added advantage to customers to buy a product with added suggestions in the form of reviews. Obviously, reviews are useful and impactful for customers who are going to buy the products. But these enormous amounts of reviews also create problems for customers as they are not able to segregate useful ones. Regardless, these immense proportions of reviews make an issue for customers as it becomes very difficult to filter informative reviews. This proportional issue has been attempted in this project. The approach that we discuss in detail later ranks reviews based on their relevance with the product and rank down irrelevant reviews.

This work has been done in four phases- data preprocessing/filtering (which includes Language Detection, Gibberish Detection, Profanity Detection), feature extraction, pairwise review ranking, and classification. The outcome will be a list of reviews for a particular product ranking on the basis of relevance using a pairwise ranking approach.

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Curriculum For This Mini Project

Business Problem - Product Reviews
08m
Solution - Workflow
05m
Dataset - Exploratory Data Analysis
05m
Data Preprocessing
11m
Feature Engineering - 1
05m
Feature Engineering - 2
07m
EDA after Feature Engineering
07m
What is Pairwise Ranking
05m
Model Training - Spot Checking
07m
Model Ranking Metric
05m
Data pipeline for deployment
09m