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1 – 2 of 2Jun Liu, Sike Hu, Fuad Mehraliyev, Haiyue Zhou, Yunyun Yu and Luyu Yang
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…
Abstract
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.
Details
Keywords
Abstract
Purpose
Sharing and disseminating debunking information are critical to correcting rumours and controlling disease when dealing with public health crises. This study investigates the factors that influence social media users' debunking information sharing behaviour from the perspective of persuasion. The authors examined the effects of argument adequacy, emotional polarity, and debunker's identity on debunking information sharing behaviour and investigated the moderating effects of rumour content and target.
Design/methodology/approach
The model was tested using 150 COVID-19-related rumours and 2,349 original debunking posts on Sina Weibo.
Findings
First, debunking information that contains adequate arguments is more likely to be reposted only when the uncertainty of the rumour content is high. Second, using neutral sentiment as a reference, debunking information containing negative sentiment is shared more often regardless of whether the government is the rumour target, and information containing positive sentiment is more likely to be shared only when the rumour target is the government. Finally, debunking information published by government-type accounts is reposted more often and is enhanced when the rumour target is the government.
Originality/value
The study provides a systematic framework for analysing the behaviour of sharing debunking information among social media users. Specifically, it expands the understanding of the factors that influence debunking information sharing behaviour by examining the effects of persuasive cues on debunking information sharing behaviour and the heterogeneity of these effects across various rumour contexts.
Details