Ranking of Product Reviews By Relevance

In the days when the internet did not exist, the early millennials or the generation before that mostly made their purchases depending upon the word of mouth they received from their friends and family. People had to make do with the sub-par quality of the products because there was no way of verifying if those reviews were personal or if it was something these people had “just heard somewhere”. With the wildfire that internet has become, shopping is mostly done online. But how do you know if the online listed products are worth your money and time? That’s where product reviews come into play. A sacred place where you know whether the reviews are provided by the “verified purchases” or they are just given by a random website user. Let’s dive into the machine learning-powered world of the reviews and see what work goes on behind the screen of your laptop or cell phones!

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. 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, 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.


Outline/Structure of the Demonstration

My presentation would talk about:

Data Preprocessing: Filtering of reviews which contain Profanity, Gibberish Text, poorly written text, etc.

Feature Engineering from Textual/ Reviews Data.

Pairwise Learn to Rank Model

How pairwise ranking helps us to rank reviews where most relevant ones come on top over irrelevant reviews?

Break up for the problem of the Pairwise Ranking of Review.
1. Why this problem and literature survey - 7mins
2. Dataset and Reviews Preprocessing. - 4mins
3. Feature Engineering/Extraction from Reviews - 5mins
4. Pairwise Ranking, Result- Metrics and Ranked Reviews Sample - 5mins

Learning Outcome

Learning to Rank: On Textual Data

Filtering Mechanism

Feature engineering on Text Data

Target Audience

Data Scientist


schedule Submitted 1 year ago

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