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

Al Amin, Anik Sarkar, Md Mahamodul Islam, Asif Ahammad Miazee, Md Robiul Islam, Md Mahmudul Hoque

TL;DR

The paper tackles Bangla sentiment polarity analysis of food reviews by building a Bengali dataset from multiple online platforms and evaluating a spectrum of machine learning and deep learning models. It employs feature extraction techniques such as CountVectorizer, TF-IDF with unigram/bigram, and GloVe Bengali vectors, and compares traditional classifiers with neural architectures. The key finding is that Logistic Regression with TF-IDF unigram features achieves 90.91% accuracy, outperforming other models on this Bangla food-review dataset. This work advances practical understanding of Bengali sentiment analysis for online food reviews and points to transformer-based methods and larger datasets as promising future directions for improved performance and multi-class classification.

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.

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

TL;DR

The paper tackles Bangla sentiment polarity analysis of food reviews by building a Bengali dataset from multiple online platforms and evaluating a spectrum of machine learning and deep learning models. It employs feature extraction techniques such as CountVectorizer, TF-IDF with unigram/bigram, and GloVe Bengali vectors, and compares traditional classifiers with neural architectures. The key finding is that Logistic Regression with TF-IDF unigram features achieves 90.91% accuracy, outperforming other models on this Bangla food-review dataset. This work advances practical understanding of Bengali sentiment analysis for online food reviews and points to transformer-based methods and larger datasets as promising future directions for improved performance and multi-class classification.

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.
Paper Structure (11 sections, 6 equations, 4 figures, 5 tables)

This paper contains 11 sections, 6 equations, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. RELATED WORKS
  3. METHODOLOGY
  4. Data Acquisition
  5. Data Cleaning and Pre-processing
  6. Features Distillation
  7. Sentiment Polarity Models
  8. Model Implementation
  9. EXPERIMENTAL RESULTS
  10. Machine Learning Model Evaluation
  11. CONCLUSION AND FUTURE WORK

Figures (4)

  • Figure 1: Proposed Methodology Workflows.
  • Figure 2: Confusion Matrix of Proposed Model
  • Figure 3: ROC Curve of Proposed Model
  • Figure 4: Precision, Recall Curve of Proposed Model