Behind the ratings: Text mining of restaurant customers’ online reviews

Jia, Susan

doi:10.1177/1470785317752048

Cited by 54 publications

(48 citation statements)

References 36 publications

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“…The data generated from food delivery apps can be used for optimizing delivery route for reducing the time consumption of the consumers, identifying individual's ordering behavior online, and improving food services. For example, Jia () examined restaurant customers’ ratings and reviews, and identified high‐frequency words, major topics, and subtopics using text mining. It has proven that digital text data analytics is a cost‐effect approach for restaurants to gain quality improvement ideas from customers.…”

Section: Application Of Text Data In Food‐related Studiesmentioning

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

154

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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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Section: Application Of Text Data In Food‐related Studiesmentioning

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

154

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“…In hospitality and tourism studies, sentiment analysis has been the most popular application in automated text analysis [8,9,12]. However, review sentiments can be redundant due to most review platforms (such as Airbnb, TripAdvisor, and online travel agencies) requiring numeric ratings, which can also serve as an accurate representation of the overall sentiment [15,18,49]. In addition, a single written review often consists of different topics and mixtures of sentiments [16].…”

Section: Automated Text Analysismentioning

confidence: 99%

A Comparative Automated Text Analysis of Airbnb Reviews in Hong Kong and Singapore Using Latent Dirichlet Allocation

Kiatkawsin

Sutherland

Kim³

2020

Sustainability

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Airbnb has emerged as a platform where unique accommodation options can be found. Due to the uniqueness of each accommodation unit and host combination, each listing offers a one-of-a-kind experience. As consumers increasingly rely on text reviews of other customers, managers are also increasingly gaining insight from customer reviews. Thus, this present study aimed to extract those insights from reviews using latent Dirichlet allocation, an unsupervised type of topic modeling that extracts latent discussion topics from text data. Findings of Hong Kong’s 185,695 and Singapore’s 93,571 Airbnb reviews, two long-term rival destinations, were compared. Hong Kong produced 12 total topics that can be categorized into four distinct groups whereas Singapore’s optimal number of topics was only five. Topics produced from both destinations covered the same range of attributes, but Hong Kong’s 12 topics provide a greater degree of precision to formulate managerial recommendations. While many topics are similar to established hotel attributes, topics related to the host and listing management are unique to the Airbnb experience. The findings also revealed keywords used when evaluating the experience that provide more insight beyond typical numeric ratings.

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“…In that properties of service, therefore, perceived service evaluation or recommendation can be crucial indicators in the field of aviation industry. Jia [13] has conducted text mining though a Chinese crowd-sourced online review community, and 49,080 pairs of restaurant ratings and reviews were examined, with high-frequency words, major topics, and subtopics identified. After text mining, multilinear regression was employed to screen out the most impactful factors that influence taste, environment, and service ratings.…”

Section: Consumers' Evaluation Criteria Onmentioning

confidence: 99%

The Identification of Marketing Performance Using Text Mining of Airline Review Data

Jiang

Park

2019

Mobile Information Systems

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We are aim firstly to extract major keywords using text mining method, secondly to identify prominent keyword from the keywords extracted from text mining analysis, and then to confirm differences in influences of the keywords which affect corporate performance. Results were as following. First, keywords have been found to show distinctive features. Since the keywords posted from the clients showed certain tendency, airlines accordingly need service management by identifying the service property through keyword analysis. Second, prominent keywords have been found out of the keyword extracted from text mining. Some of the keywords have significantly correlated with marketing performance, but others not. This implies that the company could uncover consumers’ needs through the prominent keywords and managing the properties related to the prominent keywords would help with improving corporate performance. Third, “recommend” should be treated distinctively with “satisfaction” in terms of service management through the keywords. Results suggest strategic implications to the practical business environment by analyzing keywords around the industry using text mining. We believe this work, which aims to establish common ground for understanding these analyses across multiple disciplinary perspectives, will encourage further research and development of service industry.

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Behind the ratings: Text mining of restaurant customers’ online reviews

Cited by 54 publications

References 36 publications

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

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

A Comparative Automated Text Analysis of Airbnb Reviews in Hong Kong and Singapore Using Latent Dirichlet Allocation

The Identification of Marketing Performance Using Text Mining of Airline Review Data

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