Research on Online Review Sentiment of Publicly Listed Restaurant Companies in Taiwan

Author : Nan-Yu Wang

Abstract :Restaurant reviews of publicly listed restaurant companies in Taiwan can serve as benchmarks for restaurant entrepreneurs. This study analyzes the sentiment of online reviews from customers after dining to clarify the impact of the differences in sentiment between foreign language reviews and local language reviews on restaurants. Customer review sentiment data is extracted from Google Maps for publicly listed restaurant companies in Taiwan. The research method is the machine learning classifier SVM, which divides the review data into positive or negative reviews to determine user preferences for items and extracts the "cuisine-attribute opinion-sentiment" quadruple from restaurant reviews to help merchants improve their services precisely. The expected results of the study are that people's sentiment will affect restaurant operations. Reviews using local languages may be more influential than those using foreign languages. Restaurant entrepreneurs should actively manage online reviews to help improve restaurant performance.

Keywords :Taiwanese Cuisine, Publicly Listed Companies, Review Sentiment, Machine Learning, Language

Conference Name :International Conference on Discourse Studies (ICDS-25)

Conference Place London, UK

Conference Date 2nd Aug 2025