Sentiment Polarity Analysis of Bangla Food Reviews Using Machine and Deep Learning Algorithms

TL;DR

This paper develops a machine learning model to analyze Bangla food reviews and accurately predict food quality, aiding consumers in making informed ordering decisions.

Contribution

It introduces a dataset of Bangla food reviews and evaluates multiple algorithms, identifying logistic regression as the most effective for sentiment analysis.

Findings

Logistic regression achieved 90.91% accuracy.

The dataset includes 1484 reviews from major platforms.

Deep learning methods were compared but less accurate.

Abstract

The Internet has become an essential tool for people in the modern world. Humans, like all living organisms, have essential requirements for survival. These include access to atmospheric oxygen, potable water, protective shelter, and sustenance. The constant flux of the world is making our existence less complicated. A significant portion of the population utilizes online food ordering services to have meals delivered to their residences. Although there are numerous methods for ordering food, customers sometimes experience disappointment with the food they receive. Our endeavor was to establish a model that could determine if food is of good or poor quality. We compiled an extensive dataset of over 1484 online reviews from prominent food ordering platforms, including Food Panda and HungryNaki. Leveraging the collected data, a rigorous assessment of various deep learning and machine learning techniques was performed to determine the most accurate approach for predicting food quality. Out of all the algorithms evaluated, logistic regression emerged as the most accurate, achieving an impressive 90.91% accuracy. The review offers valuable insights that will guide the user in deciding whether or not to order the food.