Hierarchical Spatiotemporal Aspect-Based Sentiment Analysis for Chain Restaurants using Machine Learning

Abstract

In recent years, aspect-based sentiment analysis of restaurant business reviews has emerged as a pivotal area of research in natural language processing (NLP), aiming to provide detailed analytical methods benefiting both consumers and industry professionals. This study introduces a novel approach, Hierarchical Spatiotemporal Aspect-Based Sentiment Analysis (HISABSA), which combines lexicon-based methods such as VADER Lexicon, the AFFIN model, and TextBlob with contextual methods. By integrating advanced machine learning (ML) techniques, this hybrid methodology facilitates sentiment analysis, empowering chain restaurants to assess changes in sentiments towards specific aspects of their services across different branches and over time. Leveraging transformer-based models such as RoBERTa and BERT, this approach achieves effective sentiment classification and aspect extraction from text reviews. The results demonstrate the reliability of extracting valid aspects from online reviews of specific branches, offering valuable insights to business owners striving to succeed in competitive markets.