Summary of Sentiment Polarity Analysis Of Bangla Food Reviews Using Machine and Deep Learning Algorithms, by Al Amin et al.
Sentiment Polarity Analysis of Bangla Food Reviews Using Machine and Deep Learning Algorithms
by Al Amin, Anik Sarkar, Md Mahamodul Islam, Asif Ahammad Miazee, Md Robiul Islam, Md Mahmudul Hoque
First submitted to arxiv on: 3 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the development of a model for predicting food quality from online reviews. A dataset of over 1484 reviews from prominent food ordering platforms was compiled, and various deep learning and machine learning techniques were evaluated to determine the most accurate approach. Logistic regression emerged as the top-performing algorithm, achieving an impressive 90.91% accuracy. The paper aims to provide insights for customers to make informed decisions about their online food orders. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about creating a tool that can predict if food ordered from online services will be good or not. Researchers collected reviews from people who used these services and tested different ways of analyzing the data to find the most accurate method. They found that using something called logistic regression worked best, with an accuracy rate of 90.91%. The goal is to help people make better choices when ordering food online. |
Keywords
» Artificial intelligence » Deep learning » Logistic regression » Machine learning
