More than words: Do emotional content and linguistic style matching matter on restaurant review helpfulness?

Wang, Xi; Tang, Liang; Kim, Eojina

doi:10.1016/j.ijhm.2018.08.007

Cited by 111 publications

(115 citation statements)

References 72 publications

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“…Therefore, we compared our approach with other recent approaches in Table 9. All of the studies [61][62][63][64][65][66][67][68] are very recent and are targeting different problem domains by using online reviews. All the studies either detect sentiments or the credibility of a review.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Topic Predictions and Optimized Recommendation Mechanism Based on Integrated Topic Modeling and Deep Neural Networks in Crowdfunding Platforms

Shafqat

Byun

2019

Applied Sciences

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The accelerated growth rate of internet users and its applications, primarily e-business, has accustomed people to write their comments and reviews about the product they received. These reviews are remarkably competent to shape customers' decisions. However, in crowdfunding, where investors finance innovative ideas in exchange for some rewards or products, the comments of investors are often ignored. These comments can play a markedly significant role in helping crowdfunding platforms to battle against the bitter challenge of fraudulent activities. We take advantage of the language modeling techniques and aim to merge them with neural networks to identify some hidden discussion patterns in the comments. Our objective is to design a language modeling based neural network architecture, where Recurrent Neural Networks (RNN) Long Short-Term Memory (LSTM) is used to predict discussion trends, i.e., either towards scam or non-scam. LSTM layers are fed with latent topic distribution learned from the pre-trained Latent Dirichlet Allocation (LDA) model. In order to optimize the recommendations, we used Particle Swarm Optimization (PSO) as a baseline algorithm. This module helps investors find secure projects to invest in (with the highest chances of delivery) within their preferred categories. We used prediction accuracy, an optimal number of identified topics, and the number of epochs, as metrics of performance evaluation for the proposed approach. We compared our results with simple Neural Networks (NNs) and NN-LDA based on these performance metrics. The strengths of both integrated models suggest that the proposed model can play a substantial role in a better understanding of crowdfunding comments.

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Topic Predictions and Optimized Recommendation Mechanism Based on Integrated Topic Modeling and Deep Neural Networks in Crowdfunding Platforms

Shafqat

Byun

2019

Applied Sciences

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“…They conducted a meta-analysis and found that review readability and review ratings did not significantly influence review helpfulness. However, it is observed that the aforementioned determinants of review helpfulness are dominantly featured by numerical features [15]. In recent years, research on review helpfulness has focused on semantic features and linguistic style in online reviews [15,40].…”

Section: Studies On Review Helpfulness Predictionmentioning

confidence: 99%

“…However, it is observed that the aforementioned determinants of review helpfulness are dominantly featured by numerical features [15]. In recent years, research on review helpfulness has focused on semantic features and linguistic style in online reviews [15,40]. Table 1 summarizes the studies on predicting review helpfulness on two popular business review sites (Yelp and Tripadvisor) during the period from 2015 to 2019.…”

Section: Studies On Review Helpfulness Predictionmentioning

confidence: 99%

“…Ngo-Ye and Sinha [41] further conducted recency (the recency of purchase), frequency (frequency or total number of purchases), and the average amount spent per transaction (monetary value) (RFM) analysis in predicting review helpfulness. In addition to widely examined review and reviewer-specific characteristics, other factors influencing review helpfulness include review orders [42], emotions, and linguistic styles [15], temporal, exploratory and sensory cues [43]. Restaurants in New York City, Las Vegas, and Los Angeles are most likely to be selected as study samples [15,37,42].…”

Section: Studies On Review Helpfulness Predictionmentioning

confidence: 99%

“…Numerous prior studies have identified factors influencing review helpfulness for both search goods (e.g., furniture, digital camera, cell phone) and experience goods (e.g., restaurant, hotel) [11,13,14]. However, these factors are dominantly measured using Likert scales or numerical metrics (e.g., review volume, star rating; sentence length) [11,15], neglecting more hidden semantic structures, such as emotions and linguistic styles conveyed through a textual content. With digital texts increasing in size, a number of studies have applied machine learning (ML), which studies a computer's ability to learn from data without being explicitly programmed [16], to predict review helpfulness.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp

Luo

2019

Sustainability

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Helpful online reviews could be utilized to create sustainable marketing strategies in the restaurant industry, which contributes to national sustainable economic development. This study, the main aspects (including food/taste, experience, location, and value) from 294,034 reviews on Yelp.com were extracted empirically using the Latent Dirichlet Allocation (LDA) and positive and negative sentiment were assigned to each extracted aspect. Positive sentiments were associated with food/taste, while negative sentiments were associated with value. This study further proves a robust classification algorithm based on Support Vector Machine (SVM) with a Fuzzy Domain Ontology (FDO) algorithm outperforms other traditional classification algorithms such as Naïve Bayes (MB) and SVM ontology in predicting the helpfulness of online reviews. This study enriches the literature on managerial aspects of sustainability by analyzing a large amount of plain text data that customers generated. The results of this study could be used as sustainable marketing strategy for review website developers to design sophisticated, intelligence review systems by enabling customers to sort and filter helpful reviews based on their preferences. The extracted aspects and their assigned sentiment could also help restaurateurs better understand how to meet diverse customers’ needs and maintain sustainable competitive advantages.

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Effects of emotions on the helpfulness of online reviews of healthy and unhealthy food in Japan and the United States

Ye,

Motoki

2024

J of Consumer Behaviour

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Online grocery shopping has become increasingly important in daily life. Consumers use the helpfulness of electronic word‐of‐mouth (eWOM) to reduce uncertainty and risk in online grocery shopping. Emotions such as positive arousal (e.g., energised) and calmness (e.g., relaxed) are often found in the eWOM of food products. However, little is known about the influence of emotions on eWOM helpfulness and how the effects of emotions on the helpfulness of online reviews differ in cultures. This study conducted an experiment to examine the impact of positive arousal and calmness on the helpfulness of eWOM regarding healthy and unhealthy foods in Japan and the USA. We found that cultural factors influenced the effect of arousal on eWOM helpfulness. For Japanese participants, positive eWOM with low arousal was more helpful than that with high arousal. In contrast, for US participants, positive eWOM with high arousal was more helpful than that with low arousal. In both countries, credibility mediated the relationship between arousal and review helpfulness. This study extends the literature on eWOM helpfulness and affect valuation theory. Our findings provide insights for companies on using eWOM in advertising campaigns and for consumers on writing helpful eWOM.

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More than words: Do emotional content and linguistic style matching matter on restaurant review helpfulness?

Cited by 111 publications

References 72 publications

Topic Predictions and Optimized Recommendation Mechanism Based on Integrated Topic Modeling and Deep Neural Networks in Crowdfunding Platforms

Topic Predictions and Optimized Recommendation Mechanism Based on Integrated Topic Modeling and Deep Neural Networks in Crowdfunding Platforms

Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp

Effects of emotions on the helpfulness of online reviews of healthy and unhealthy food in Japan and the United States

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