Sentiment Analysis on Bangla Food Reviews Using Machine Learning and Explainable NLP
Md. Shymon Islam, Kazi Masudul Alam
Abstract
Sentiment analysis (SA) is a sub-field of natural language processing (NLP) which can extract valuable insights from textual data of a language. Food review analysis is a trending domain of SA which become very useful as internet dependency has rapidly shifted people’s food ordering preferences from restaurants to online platforms. This work focuses on examining various machine learning (ML) and deep learning (DL) algorithms for Bangla sentimental analysis on food reviews using a new dataset of 44,491 reviews collected from various restaurant Facebook pages and groups. Furthermore, in this study, we utilized the explainable NLP to interpret why a model is performing well or poor. Random Forest (RF) and Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) models outperformed other models and achieved the highest accuracy of 88.73% and 90.96% from ML and DL domains respectively. Friedman statistical test was performed on the obtained results and the test results are significant at p<0.05. "দর" is the best feature that is responsible for the hybrid DL (CNN-BiGRU) model to classify reviews more accurately.