Food Review Sentiment Analysis

Business Problem

A food product will have multiple reviews from multiple websites with rating or without rating but only text. As a product owner I want to understand the count or percentage of total Positive, Neutral and Negative reviews for the product.

Overview

An ML model which can analyzing hundreds of food reviews and return the percentage of Positive, Neutral and Negative reviews.

• **Deployed web app :**https://praathapj-foodreviewsentimentana-foodreviewsentiment-app-e1nreg.streamlit.app/

Data Collection

Real world data obtained from kaggle: https://www.kaggle.com/datasets/snap/amazon-fine-food-reviews

Data Cleaning and Preprocessing

• Removed non realstic data and joined review summery and text as one single feature.
• Aim was to predict Sentiment hence processed rating 5,4 as Positive, 3 as neutral and 2,1 as Negative.
• Considered only the text data to predict sentiment.

Data Analysis

• Highly imbalanced dataset, positive reviews were more than twice compared to other two class.
• Performed random Undersampling and oversampling with median samples of distribution.

Features Engineering or Vectoriztion

• Performed Bag Of Words(BOW), n gram BOW, Binary BOW, TF-IDF, Word 2 Vector(W2V), avg W2V and TF-IDF W2V
• All the vector were scaled between 0 to 1

Modelling

Very high dimensional data approx 5000 features, hence Naive bayese was used as it works very well and performance constraints.

Model Evaluation

• Evaluation metric is multi class log loss and confusino matrics.
• Random model was used to baseline the max log loss: 0.69
• TF-IDF gave the least log loss 0.58

Improvement

• Use more balanced data than sampling and try with other algorithms which requires huge computation.