Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews

Li, Hengyun; Yu, Bruce X. B.; Li, Gang; Gao, Huicai

doi:10.1016/j.tourman.2022.104707

Cited by 62 publications

(25 citation statements)

References 109 publications

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“…In terms of analyzing granularity, SA is divided into three main categories, namely, document-level, sentence-level, and aspect-based SA [13]. For SA based on online reviews, the methods utilized are classified into three main categories: machine learning-based SA [14], lexicon-based SA [15], and deep learningbased SA [16]. Darko et al [4] used another unsupervised VADER algorithm that scores online comments and divides them into five score bands to distinguish different sentiment polarities.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Online review–driven Product Recommendation Ranking Considering Interactions Among Features and Consumers’ Psychology

Liu,

Li,

Chin

2024

Preprint

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In the digital economy, online reviews serve as valuable and real-time feedback for consumers, making them a crucial source of information for product selection. We propose a product recommendation ranking method based on sentiment analysis of online reviews to address information overload and support informed purchase decisions. The proposed model considers the interdependencies among product features and fully simulates the psychological state of consumers before and after purchasing products. First, online reviews of products are mined and preprocessed. Natural language processing techniques are employed to extract product features and perform aspect-based sentiment analysis. The results are transformed into an evaluation matrix of the probabilistic linguistic term sets (PLTSs). Then, we propose a 2-additive fuzzy measures recognition model based on multi-dimensional feature attention information to effectively identify redundant, independent, and complementary interactions among product features. Subsequently, the fuzzy weight and the proposed possibility comprehensive discrepancy measures are applied to the interactive multi-criteria decision-making based on CPT-TODIM (Cumulative Prospect Theory TODIM) method to rank the recommended priorities of alternative products. The feasibility of the proposed method is validated through a case study of smart sweeping robot products from Jingdong Mall, followed by the investigation of the impact of various psychological behaviours that influence purchase decisions on the recommendation ranking based on stochastic multi-criteria acceptability analysis (SMAA). Finally, a comparative analysis is conducted to demonstrate the advantages and effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Online review–driven Product Recommendation Ranking Considering Interactions Among Features and Consumers’ Psychology

Liu,

Li,

Chin

2024

Preprint

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“…Negative reviews can have the opposite impact of what positive ones might, harming a restaurant's reputation and driving away potential customers. Decision making with the help of new technology is important to solve the socio-economic problems [31][32][33][34][35][36]. Therefore, restaurant owners and managers are increasingly focusing on improving their online reputation and customer reviews to enhance their business performance [36][37][38].…”

Section: Methodsmentioning

confidence: 99%

“…Decision making with the help of new technology is important to solve the socio-economic problems [31][32][33][34][35][36]. Therefore, restaurant owners and managers are increasingly focusing on improving their online reputation and customer reviews to enhance their business performance [36][37][38]. They are adopting various strategies such as improving the quality of food and service, engaging with customers through social media, and incentivizing customers to leave reviews on online platforms like Zomato.…”

Section: Methodsmentioning

confidence: 99%

Customer experience and satisfaction: Importance of customer reviews and customer value on buying preference

2023

IRJMETS

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With the emergence of internet platforms like Zomato, and Swiggy the restaurant business has seen a considerable upheaval over the last few years. Customers now obtain details about other eateries, including their menus and client ratings, thanks to these portals. Customers now extensively rely on customer evaluations when choosing a restaurant because they have become a crucial component of their decision-making process. This study intends to examine how restaurant ratings on Zomato, specifically in the Navi Mumbai region, affect consumers' preferences for eateries. The study examines the connection between patron evaluations, restaurant evaluations, and purchasing preferences. The study combines a qualitative examination of customer reviews from Zomato with a quantitative analysis of data gathered through questionnaire survey. The study shed light on the variables that affect patrons' decisions when choosing a restaurant and the significance of patron reviews in this process. This study will be helpful for restaurants operating on Zomato to understand how online reviews plays a major part in the buying behaviour of a consumer and effective business development.

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“…Punetha and Jain [14] developed an innovative framework using unsupervised learning for sentiment analysis of TripAdvisor and Yelp restaurant reviews. In Li et al [15], researchers employed a Conditional Survival Forest (CSF) model in machine learning to categorize the overall sentiment of online reviews. Luo and Xu [16] utilized Bidirectional LSTM and Simple Embedding with Average Pooling for sentiment classification during the COVID pandemic.…”

Section: Sentiment Analysis In Restaurant Reviewsmentioning

confidence: 99%

Opinion distribution: spatial sentiment analysis of online restaurant reviews through BERT model and GIS

Niu,

Xing

2024

Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024)

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As the impact of online reviews on consumer decision-making grows more pronounced, this study is dedicated to identifying service-related issues by analyzing online restaurant customer reviews. We utilized user reviews from the "Beijing Must-Eat List" on DianPing as the data source, employing artificial intelligence alongside spatial geographic analysis methods. Reviews were categorized for sentiment using the Bidirectional Encoder Representations from Transformers (BERT) model, with word clouds created for a visual display. The study also integrates hotspot estimation and kernel density estimation from spatial geographic analysis to delve into the geographic characteristics of sentiments in reviews. The model's effectiveness was assessed using metrics such as Precision, Recall, and F-Measure. Results indicated that our model excelled, demonstrating a precision of 98.73%, recall rate of 91.06%, and an F-Measure of 94.74%. This research offers insightful contributions towards a more nuanced understanding of consumer preferences and enhancing the marketing strategies in the food and beverage sector.

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Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews

Cited by 62 publications

References 109 publications

RETRACTED: Online review–driven Product Recommendation Ranking Considering Interactions Among Features and Consumers’ Psychology

RETRACTED: Online review–driven Product Recommendation Ranking Considering Interactions Among Features and Consumers’ Psychology

Customer experience and satisfaction: Importance of customer reviews and customer value on buying preference

Opinion distribution: spatial sentiment analysis of online restaurant reviews through BERT model and GIS

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