Purpose

This study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, thus advancing the literature and providing practical insights into electronic word-of-mouth management for the industry.

Design/methodology/approach

This study elaborates a hybrid model that integrates deep learning (DL) and a sentiment lexicon (SL) and compares it to five other models, including SL, random forest (RF), naïve Bayes, support vector machine (SVM) and a DL model, for the task of emotion recognition in restaurant online reviews. These models are trained and tested using 652,348 online reviews from 548 restaurants.

Findings

The hybrid approach performs well for valence-based emotion and discrete emotion recognition and is highly applicable for mining online reviews in a restaurant setting. The performances of SL and RF are inferior when it comes to recognizing discrete emotions. The DL method and SVM can perform satisfactorily in the valence-based emotion recognition.

Research limitations/implications

These findings provide methodological and theoretical implications; thus, they advance the current state of knowledge on emotion recognition in restaurant online reviews. The results also provide practical insights into intelligent service quality monitoring and electronic word-of-mouth management for the industry.

Originality/value

This study proposes a superior model for emotion recognition in restaurant online reviews. The methodological framework and steps are elucidated in detail for future research and practical application. This study also details the performances of other commonly used models to support the selection of methods in research and practical applications.

中文翻译：

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识别餐厅在线评论中的情绪：集成深度学习和情感词典的混合模型

目的

本研究旨在建立一个快速准确的餐厅在线评论情感识别模型，从而推进文献的发展，并为行业的电子口碑管理提供实用的见解。

设计/方法论/途径

本研究阐述了一种集成深度学习 (DL) 和情感词典 (SL) 的混合模型，并将其与其他五个模型进行比较，包括 SL、随机森林 (RF)、朴素贝叶斯、支持向量机 (SVM) 和 DL 模型，用于餐厅在线评论中的情感识别任务。这些模型使用来自 548 家餐厅的 652,348 条在线评论进行训练和测试。

发现

该混合方法对于基于价的情感和离散情感识别表现良好，并且非常适用于挖掘餐厅环境中的在线评论。在识别离散情绪方面，SL 和 RF 的表现较差。DL方法和SVM在基于效价的情感识别中可以取得令人满意的效果。

研究局限性/影响

这些发现提供了方法论和理论意义；因此，他们推进了餐厅在线评论中情感识别的当前知识水平。研究结果还为行业智能服务质量监控和电子口碑管理提供了实用见解。

原创性/价值

这项研究提出了一种用于餐厅在线评论中的情感识别的高级模型。详细阐明了方法框架和步骤，以供将来的研究和实际应用。本研究还详细介绍了其他常用模型的性能，以支持研究和实际应用中方法的选择。