Opinion distribution: spatial sentiment analysis of online restaurant reviews through BERT model and GIS

Kehan Niu; Yuhui Xing

doi:10.1117/12.3027179

1 April 2024 Opinion distribution: spatial sentiment analysis of online restaurant reviews through BERT model and GIS

Kehan Niu, Yuhui Xing

Proceedings Volume 13077, Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024); 130770N (2024) https://doi.org/10.1117/12.3027179
Event: 4th International Conference on Signal Processing and Machine Learning (CONF-SPML 2024), 2024, Chicago, IL, United States

Abstract

As the impact of online reviews on consumer decision-making grows more pronounced, this study is dedicated to identifying service-related issues by analyzing online restaurant customer reviews. We utilized user reviews from the "Beijing Must-Eat List" on DianPing as the data source, employing artificial intelligence alongside spatial geographic analysis methods. Reviews were categorized for sentiment using the Bidirectional Encoder Representations from Transformers (BERT) model, with word clouds created for a visual display. The study also integrates hotspot estimation and kernel density estimation from spatial geographic analysis to delve into the geographic characteristics of sentiments in reviews. The model's effectiveness was assessed using metrics such as Precision, Recall, and F-Measure. Results indicated that our model excelled, demonstrating a precision of 98.73%, recall rate of 91.06%, and an F-Measure of 94.74%. This research offers insightful contributions towards a more nuanced understanding of consumer preferences and enhancing the marketing strategies in the food and beverage sector.

Citation Download Citation

Kehan Niu and Yuhui Xing "Opinion distribution: spatial sentiment analysis of online restaurant reviews through BERT model and GIS", Proc. SPIE 13077, Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024), 130770N (1 April 2024); https://doi.org/10.1117/12.3027179

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