A Text Mining and Multidimensional Sentiment Analysis of Online Restaurant Reviews

Gan, Qiwei; Ferns, Bo H.; Yu, Yang; Jin, Lei

doi:10.1080/1528008x.2016.1250243

Cited by 108 publications

(93 citation statements)

References 83 publications

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“…Good taste and food quality are more likely to generate positive online reviews. However, this study highlights that customers tend to express negative emotions towards value, which is different from previous findings indicating restaurant ambience had the lowest and still positive sentiment score [78], and customers tend to complain about service quality [79]. The potential reason might be this study extracted restaurant reviews from three metropolitan cities in the U.S. where the costs of living are relatively high.…”

Section: Summary Of Results and Discussioncontrasting

confidence: 87%

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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Section: Summary Of Results and Discussioncontrasting

confidence: 87%

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

Luo

2019

Sustainability

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“…Nevertheless, the proposed approach provides more insights into the dining behavior and opinions of tourists than prior works using restaurant reviews (Gan et al 2016;Zhang et al 2014;Zhang et al 2017) because textual reviews are analyzed. The results do not rely on user rating of restaurant attributes and other social information.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, Zhang Zhang, and Law (2014) investigated the relationship between the attribute performance of a restaurant and the positive and negative word of mouth of customers. Gan et al (2016) used review data on Yelp to examine the structure of reviews and the influence of review attributes and sentiments on restaurant ratings. Zhang et al (2017) used restaurant review in a case study to establish a decision support model to assist tourists in selecting restaurants.…”

Section: Restaurant Review Analysismentioning

confidence: 99%

Exploring Tourist Dining Preferences Based on Restaurant Reviews

Law

et al. 2017

Journal of Travel Research

116

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Dining is an essential tourism component that attracts significant expenditure from tourists. Tourism practitioners need insights into the dining behaviors of tourists to support their strategic planning and decision making. Traditional surveys and questionnaires are time consuming and inefficient in capturing the complex dining behaviors of tourists at a large scale. Thus far, the understanding about the dining preferences and opinions of different tourist groups is limited. This paper aims to fill the void by presenting a method that utilizes online restaurant reviews and text processing techniques in analyzing the dining behaviors of tourists. The effectiveness of the proposed method is demonstrated in a case study on international tourists visiting Australia using a large-scale data set of more than 40,000 restaurant reviews made by tourists on 2,265 restaurants. The proposed method can help researchers gain comprehensive insights into the dining preferences of tourists.

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“…In this case, the problem is formulated as regression problem. In the food industry, sentiment analysis has been used to know about the performances of food products and services from customer reviews (Gan, Ferns, Yu, & Jin, 2017;Hayashi, Hsieh, & Setiono, 2009).…”

Section: Advanced Text Analysismentioning

confidence: 99%

Utilization of text mining as a big data analysis tool for food science and nutrition

Tao

Yang

Feng

2020

Comp Rev Food Sci Food Safe

166

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Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument‐generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word‐level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer‐opinion mining, new‐product development, food knowledge discovery, food supply‐chain management, and online food services. The goal is to provide insights for intelligent decision‐making to improve food production, food safety, and human nutrition.

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A Text Mining and Multidimensional Sentiment Analysis of Online Restaurant Reviews

Cited by 108 publications

References 83 publications

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

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

Exploring Tourist Dining Preferences Based on Restaurant Reviews

Utilization of text mining as a big data analysis tool for food science and nutrition

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