Sentiment Polarity Analysis of Bangla Food Reviews Using Machine and Deep Learning Algorithms
This work addresses the issue of customer disappointment with food delivery services by providing a tool for quality prediction, but it is incremental as it applies existing methods to a new dataset.
The researchers tackled the problem of predicting food quality from Bangla online reviews by evaluating various machine and deep learning algorithms, with logistic regression achieving the highest accuracy of 90.91%.
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.