Improving Sentiment Classification of Restaurant Reviews with Attention-Based Bi-GRU Neural Network

Liangqiang Li,Yuyang Zeng,Liang Yang

doi:10.3390/sym13081517

Abstract

In the era of Web 2.0, there is a huge amount of user-generated content, but the huge amount of unstructured data makes it difficult for merchants to provide personalized services and for users to extract information efficiently, so it is necessary to perform sentiment analysis for restaurant reviews. The significant advantage of Bi-GRU is the guaranteed symmetry of the hidden layer weight update, to take into account the context in online restaurant reviews and to obtain better results with fewer parameters, so we combined Word2vec, Bi-GRU, and Attention method to build a sentiment analysis model for online restaurant reviews. Restaurant reviews from Dianping.com were used to train and validate the model. With F1-score greater than 89%, we can conclude that the comprehensive performance of the Word2vec+Bi-GRU+Attention sentiment analysis model is better than the commonly used sentiment analysis models. We applied deep learning methods to review sentiment analysis in online food ordering platforms to improve the performance of sentiment analysis in the restaurant review domain.

Highlights

The widespread adoption of Web 2.0 has provided an environment for consumers to engage in expression, creativity, communication, and sharing
We propose a deep learning-based sentiment analysis framework for otonlsiennetrimesetanutraannatlryesvisietwecsh. nTihqeureesseuasricnhgfmraamchewinoerlkeaorfnthinisgpmapetehroisdssh, doweenpinleFarignuinrge-1b.aTsheids fsreanmtiemweonrtkancoanlyssisistsisomf fooruergmenaeirnalciozmabpleo,naenndtsi:n(1a)dWditeibonC, rdaeweplerle; a(r2n) iPnrge--bParsoecdesmsientgh;o(d3s) WhaovredbVetetcetropr;e(r4fo) rSmenatnimceeinnttAernmalsyosifsf.eature extraction and nonlinear fitting capabilities
The field of online reviews usually considers the textual sentiment of online reviews to be consistent with the digital review ratings [42]

Summary

Introduction

The widespread adoption of Web 2.0 has provided an environment for consumers to engage in expression, creativity, communication, and sharing. Consumers are able to post reviews on online ordering platforms (e.g., Yelp, TripAdvisor, Dianping.) in order to express their opinions about restaurants, vent their emotions, and engage in social activities. Merchants often encourage consumers to actively participate in reviews, and massive usergenerated restaurant reviews give consumers the opportunity to fully express their needs while helping merchants provide real-time and personalized service [1,2]. Due to the intangible and complex nature of goods and services in the restaurant industry, consumers rely heavily on reviews from other customers to evaluate service quality before spending money [4]. Restaurant reviews express the composition of consumers’ emotional needs and are an important source of information that consumers can refer to [5]. In the pre-consumer information search phase, consumers tend to search for a large number of restaurant reviews from other users to reduce the perceived uncertainty and perceived risk caused by information asymmetry [6]

Results

Discussion

Conclusion