Electronic word-of-mouth effects on studio performance leveraging attention-based model

Yang, Liu; Hao, Fei; Qing, Zeng; Li, Bobo; Ma, Lili; Ji, Donghong; Meré, Joaquín Bienvenido Ordieres

doi:10.1007/s00521-020-04937-0

Cited by 12 publications

(4 citation statements)

References 61 publications

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“…The effect of computing UGC using a combined approach provides new opportunities for studios' business models. Consumer sentiment through UGC remains an important communication channel (Liu et al, 2020). It can also be applied to natural language processing, computer vision, and other artificial intelligence technologies.…”

Section: Discussionmentioning

confidence: 99%

Analyzing the effect of user‐generated content on studio performance: A combined approach

Liu

2024

Manage Decis Econ

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This research explores how user‐generated content (UGC) influences the performance of studios. Our dataset included both structured and unstructured UGC, analyzed through a hybrid of statistical techniques and machine learning algorithms. The findings revealed a positive relationship between UGC and both box office revenues and stock market performance. Additionally, classical machine learning methods demonstrated exceptional capabilities in accurately classifying the data. The insights from this study offer valuable marketing strategies for studio marketing managers and studio executives in leveraging social networks effectively.

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Section: Discussionmentioning

confidence: 99%

Analyzing the effect of user‐generated content on studio performance: A combined approach

Liu

2024

Manage Decis Econ

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“…Gellerstedt and Arvemo (2019) explored the difference between the impact of e-WOM and the one of traditional WOM. Liu et al (2020) developed hierarchical attention network models. By integrating the attention mechanism into deep learning models, they found that e-WOM could provide excellent insights into the film market.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

Capturing and analyzing e-WOM for travel products: a method based on sentiment analysis and stochastic dominance

Zheng

Yue

et al. 2021

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PurposeIn recent years, electronic word-of-mouth (e-WOM) concerning travel products reflected in online review information has become an important reference for tourists to make their product purchase decisions, while for travel service providers (TSPs), monitoring and improving the e-WOM of their travel products is always an important task. Therefore, based on the online review information, how to capture e-WOM of travel products and find out specific ways to improve the e-WOM is a noteworthy research problem. The purpose of this paper is to develop a method for capturing and analyzing e-WOM toward travel products based on sentiment analysis and stochastic dominance.Design/methodology/approachSpecifically, online review information of travel products is first crawled and preprocessed. Second, sentiment strengths of online review information toward travel products concerning each feature are judged. Then, the matrix of structured online review information toward travel products is formed. Further, the matrix of e-WOM comparisons between any two travel products is constructed, and e-WOM ranking concerning each travel product is determined. Finally, trade-off chart models are constructed to conduct the e-WOM improvement analyses concerning the travel products.FindingsAn empirical study based on the online review information toward six travel products crawled from the Tuniu.com website is given to illustrate the use of the proposed method.Originality/valueThe proposed method can not only realize the real-time e-WOM monitoring to travel products but also be useful for TSPs to improve the e-WOM of their travel products.

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“…Due to the difference between implicit and explicit meanings in consumer reviews (Liu et al , 2020), the ambiguity and “context mismatch” between text and images leads to business managers' erroneous judgment of consumer sentiment. Although many researchers have analyzed customer reviews to reveal customer sentiment (Alaei et al , 2019), no one has investigated the affective differences between comment text and photographs, particularly the effect of sarcasm.…”

Section: Introductionmentioning

confidence: 99%

Sarcasm detection in hotel reviews: a multimodal deep learning approach

Liu,

Chi,

Sun

2024

JHTT

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Purpose This study aims to detect consumer sarcasm through inconsistencies in sentiment features between text and images of hotel reviews. Design/methodology/approach This paper proposes a model for sarcasm detection based on multimodal deep learning using reviews of three hotel brands collected from two travel platforms, which can identify emotional inconsistencies within a modality and across modalities. Text-image interaction information is explored using graph neural networks (GNN) to detect essential clues in sarcasm sentiment. Findings The research results show that the multimodal deep learning model outperforms other baseline models, which can help to understand hotel service evaluation and provide hotel managers with decision-making opinions. Originality/value This research can help hoteliers in two ways: detecting service quality and formulating strategies. By selecting reference hotel brands, hoteliers can better assess their level of service quality (optimal resource allocation ensues); therefore, sarcasm detection research is not only beneficial for hotel managers seeking to improve service quality. The multimodal deep learning method introduced in the present study can be replicated in other industries to help travel platforms optimize their products and services.

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Electronic word-of-mouth effects on studio performance leveraging attention-based model

Cited by 12 publications

References 61 publications

Analyzing the effect of user‐generated content on studio performance: A combined approach

Analyzing the effect of user‐generated content on studio performance: A combined approach

Capturing and analyzing e-WOM for travel products: a method based on sentiment analysis and stochastic dominance

Sarcasm detection in hotel reviews: a multimodal deep learning approach

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