Sentiment Analysis using Zomato Reviews

Overview

The project uses data of customer reviews and ratings of restaurants in Bangalore (listed on Zomato), to make a model that could predict correct sentiment from a review.

Blog on the Project

Preprocessing: Data Preprocessing for Sentiment Analysis on Zomato Reviews

Network Design and Training: Sentiment Analysis on Zomato Reviews

Motivation

Reviews are related with almost every brand product or service. Sometimes it becomes necessary for a business to know how a product is being recognized by their customer. Thus, sentiment analysis which classifies text to a particular sentiment comes to help in identifying the sentiment associated with customer description of a product or service.

Task

We divide the task for this project into:

• Preprocessing: Extract the raw text from the source, and make necessary transformation and tuning to turn it to a form which works well for our model.
• Network Architecture and Training: Design our network and set up training methods which helps in building a robust sentiment analysis model.

About Data

• The Zomato dataset has 51717 instances of 17 features/columns. Out of these, only one is numeric type.
• The corpus obtained after Preprocessing has pairs of 133896 ratings and reviews.

Roadmap

Preprocessing:

• Review-Rating extraction from source data

• Textual Noise removal

• Text Normalization

• Lemmatization

• Lower frequency word removal

• Data balancing

Network Design and Training:

• Model Design (BiLSTM)

• Set up Training for Model

• Implement Batching

• Use Pre-trained embedding(glove) to the model

• Train and Evaluate model

Preprocessing Notebook

Network Design and Training Notebook

Tech Stack

• Pytorch
• pandas
• matplotlib
• numpy
• scikit learn
• nltk

Acknowledgements/Credits

The source data belongs to Zomato Ltd. and is extracted by Himanshu Poddar. Please give necessary credits if you are using the data.