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This study develops and validates an innovative approach for extracting knowledge from online user reviews by integrating textual content and emojis. Recognizing the pivotal role emojis play in enhancing the expressiveness and emotional depth of digital communication, this study aims to address the significant gap in existing sentiment analysis models, which have largely overlooked the contribution of emojis in interpreting user preferences and sentiments. By constructing a comprehensive model that synergizes emotional and semantic information conveyed through emojis and text, this study seeks to provide a more nuanced understanding of user preferences, thereby enhancing the accuracy and depth of knowledge extraction from online reviews. The goal is to offer a robust framework that enables more effective and empathetic engagement with user-generated content on digital platforms, paving the way for improved service delivery, product development and customer satisfaction through informed insights into consumer behavior and sentiments.

This study uses a structured methodology to integrate and analyze text and emojis from online reviews for effective knowledge extraction, focusing on user preferences and sentiments. This methodology consists of four key stages. First, this study leverages high-frequency noun analysis to identify and extract product attributes mentioned in online user reviews. By focusing on nouns that appear frequently, the authors can systematically discern the primary features or aspects of products that users discuss, thereby providing a foundation for a more detailed sentiment and preference analysis. Second, a foundational sentiment dictionary is established that incorporates sentiment-bearing words, intensifiers and negation terms to analyze the textual part of the reviews. This dictionary is used to assign sentiment scores to phrases and sentences within reviews, allowing the quantification of textual sentiments based on the presence and combination of these predefined lexical items. Third, an emoticon sentiment dictionary is developed to address the emotional content conveyed through emojis. This dictionary categorizes emojis based on their associated sentiments, thus enabling the quantification of emotional expressions in reviews. The sentiment scores derived from the emojis are then integrated with those from the textual analysis. This integration considers the weights of text- and emoji-based emotions to compute a comprehensive attribute sentiment score that reflects a nuanced understanding of user sentiments and preferences. Finally, the authors conduct an empirical study to validate the effectiveness of the proposed methodology in mining user preferences from online reviews by applying the approach to a data set of online reviews and evaluating its ability to accurately identify product attributes and user sentiments. The validation process assessed the reliability and accuracy of the methodology in extracting meaningful insights from the complex interplay between text and emojis. This study offers a holistic and nuanced framework for knowledge extraction from online reviews, capturing both explicit and implicit sentiments expressed by users through text and emojis. By integrating these elements, this study seeks to provide a comprehensive understanding of user preferences, contributing to improved consumer insight and strategic decision-making for businesses and researchers.

The application of the proposed methodology for integrating emojis with text in online reviews yields significant findings that underscore the feasibility and value of extracting realistic user knowledge to gain insights from user-generated content. The analysis successfully captured consumer preferences, which are instrumental in informing service decisions and driving innovation. This achievement is largely attributed to the development and utilization of a comprehensive emotion-sentiment dictionary tailored to interpret the complex interplay between textual and emoji-based expressions in online reviews. By implementing a sentiment calculation model that intricately combines textual sentiment analysis with emoji sentiment analysis, this study was able to accurately determine the final attribute emotion for various product features discussed in the reviews. This model effectively characterized the emotional knowledge of online users and provided a nuanced understanding of their sentiments and preferences. The emotional knowledge extracted is not only quantifiable but also rich in context, offering deeper insights into consumer behavior and attitudes. Furthermore, a case analysis is conducted to rigorously test the validity of the proposed model in a real-world scenario. This practical examination revealed that the model is not only capable of accurately extracting and analyzing user preferences but is also adaptable to different contexts and product categories. The case analysis highlights the robustness and flexibility of the model, demonstrating its potential to enhance the precision of knowledge extraction processes significantly. Overall, the results confirm the effectiveness of the proposed approach in integrating text and emojis for comprehensive knowledge extraction from online reviews. The findings validate the model’s capability to offer actionable insights into consumer preferences, thereby supporting more informed and strategic decision-making by businesses. This study contributes to the broader field of sentiment analysis by showcasing the untapped potential of emojis as valuable indicators of user sentiments, opening new avenues for research and applications in digital marketing and consumer behavior analysis.

This study introduces a pioneering approach to extract knowledge from Web user interactions, notably through the integration of online reviews that incorporate both textual content and emoticons. This innovative methodology stands out because it holistically considers the dual channels of communication, text and emojis, to comprehensively mine Web user preferences. The key contribution of this study lies in its novel insights into the extraction of consumer preferences, advancing beyond traditional text-based analysis to embrace nuanced expressions conveyed through emoticons. The originality of this study is underpinned by its acknowledgment of emoticons as a significant and untapped source of sentiment and preference indicators in online reviews. By effectively merging emoticon analysis and emoji emotion scoring with textual sentiment analysis, this study enriches the understanding of Web user preferences and enhances the accuracy and depth of consumer preference insights. This dual-analysis approach represents a significant leap forward in sentiment analysis, setting a new standard for how digital communication can be leveraged to derive meaningful insights into consumer behavior. Furthermore, the results have practical implications to businesses and marketers. The insights gained from this integrated analytical approach offer a more granular and emotionally nuanced view of customer feedback, which can inform more effective marketing strategies, product development and customer service practices. By pioneering this comprehensive method of knowledge extraction, this study paves the way for future research and practice to interpret and respond more accurately to the complex landscape of online consumer expressions. This study’s originality and value lie in its innovative method of capturing and analyzing the rich tapestry of Web user communication, offering a ground-breaking perspective on consumer preference extraction that promises to enhance both academic research and practical applications in the digital era.

This methodological paper demonstrates how service firms can use digital technologies to quantify and predict customer evaluations of their interactions with the firm using unstructured, qualitative data. To harness the power of unstructured data and enhance the customer-firm relationship, the use of computerized text analysis is proposed.

Three empirical studies were conducted to exemplify the use of the computerized text analysis tool. A secondary data analysis of online customer reviews (n = 2,878) in a service industry was used. LIWC was used to conduct the text analysis, and thereafter SPSS was used to examine the predictive capability of the model for the evaluation of customer-firm interactions.

A lexical analysis of online customer reviews was able to predict evaluations of customer-firm interactions across the three empirical studies. The authenticity and emotional tone present in the reviews served as the best predictors of customer evaluations of their service interactions with the firm.

Computerized text analysis is an inexpensive digital tool which, to date, has been sparsely used to analyze customer-firm interactions based on customers' online reviews. From a methodological perspective, the use of this tool to gain insights from unstructured data provides the ability to gain an understanding of customers' real-time evaluations of their service interactions with a firm without collecting primary data.

This research contributes to the growing body of knowledge regarding the use of computerized lexical analysis to assess unstructured, online customer reviews to predict customers' evaluations of a service interaction. The results offer service firms an inexpensive and user-friendly methodology to assess real-time, readily available reviews, complementing traditional customer research. A tool has been used to transform unstructured data into a numerical format, quantifying customer evaluations of service interactions.

The purpose of this paper is to introduce a new multiple imputation method that can effectively manage missing values in online review data, thereby allowing the online review analysis to yield valid results by using all available data.

This study develops a missing data method based on the multivariate imputation chained equation to generate imputed values for online reviews. Sentiment analysis is used to incorporate customers’ textual opinions as the auxiliary information in the imputation procedures. To check the validity of the proposed imputation method, the authors apply this method to missing values of sub-ratings on hotel attributes in both the simulated and real Honolulu hotel review data sets. The estimation results are compared to those of different missing data techniques, namely, listwise deletion and conventional multiple imputation which does not consider text reviews.

The findings from the simulation analysis show that the imputation method of the authors produces more efficient and less biased estimates compared to the other two missing data techniques when text reviews are possibly associated with the rating scores and response mechanism. When applying the imputation method to the real hotel review data, the findings show that the text sentiment-based propensity score can effectively explain the missingness of sub-ratings on hotel attributes, and the imputation method considering those propensity scores has better estimation results than the other techniques as in the simulation analysis.

This study extends multiple imputation to online data considering its spontaneous and unstructured nature. This new method helps make the fuller use of the observed online data while avoiding potential missing problems.

This study is part of a larger research project which aims to investigate whether sentiments in online reviews on children’s books would represent significant factors which are useful for selecting the right books for children. This paper aims to examine whether positive, negative or neutral attitude would be directly associated with the overall ratings of books.

The study investigates subjectivity and polarity of online reviews on children’s books such as neutral, positive or negative sentiment. For the investigation of a statistical association between the sentiment values and the rating scores, this study performs correlation analysis. For a clear explanation of the factors affecting the relationships between the sentiment value and the rating score, this study uses the concept-level sentiment analysis of online reviews.

The findings of this study demonstrate that there is a weak or low correlation between the sentiment value and the rating score of a book and they are hardly related for most books. The results of this study also uncover key contributing factors that affected the correlations between two variables and made the relationship weak.

This study increases awareness of the implications of online reviews as user-generated contents for complementing the existing controlled vocabulary.

This study contributes to improving library catalogs by using latent topics extracted from online reviews which provide additional access points for assisting in the selection of books.

Although several studies have conducted on online reviews in the domain of business, no research appears to exist on the sentiment analysis of online reviews about children’s books. This study attempts to address the potential and challenges associated with using online reviews to help find the right books for children.

This study aims to explore the hidden connectivity among words by semantic network analysis, further identify salient factors accounting for customer satisfaction of coffee shops through analysis of online reviews and, finally, examine the moderating effect of business types of coffee shops on customer satisfaction.

Two typical major procedures of big data analytics in the hospitality industry were adopted in this research: one is data collection and the other is data analysis. In terms of data analysis, frequency analysis with text mining, semantic network analysis, CONCOR analysis for clustering and quantitative analysis with dummy variables were performed to dig new insights from online customer reviews both qualitatively and quantitatively.

Different factors were extracted from online customer reviews contributing to customer satisfaction or dissatisfaction, and among these factors, the brand-new factor “Sales event” was examined to be significantly associated with customer satisfaction. In addition, the moderating effect of business types on the relationship between “Value for money” and customer satisfaction was verified, indicating differences between customers from different types of coffee shops.

The present study broadened the research directions of coffee shops by adopting online customer reviews through relative analytics. New dimensions such as “Sales event” and detailed categorization of “Coffee quality”, “Interior” and “Physical environment” were revealed, indicating that even new cognition could be generated with new data source and analytical methods. The industry professionals could develop their decision-making based on information from online reviews.

The present study used online reviews to understand coffee shop costumer experience and satisfaction through a set of analytical methods. The textual reviews and numeric reviews were concerned simultaneously to unearth qualitative perception and quantitative data information for customers of coffee shops.

本研究的目的在于通过网络评论分析了解网络评论中关键词之间的隐含联系, 然后探究影响咖啡店顾客满意度的因素, 最后验证不同咖啡店经营类型的调节作用。

本研究使用文本挖掘的频率分析、语义网络分析、聚类分析和通过虚拟变量进行的定量分析, 从网络评论中挖掘对咖啡店行业的新见解。此外, 还检验了咖啡店经营类型的调节作用。

从网络评论中提取影响顾客满意度的不同因素, 探索出“销售活动”对顾客满意度的显著影响。同时, 相较于连锁咖啡店, 独立经营咖啡店对“物有所值”到顾客满足度的关系具有调节作用。  

本研究通过使用相关的分析方法对顾客网络评论进行分析, 拓宽了咖啡店研究的方向。研究结果发现“销售活动”和“咖啡品质”的细分等新方面, 揭示了利用新的数据源和分析方法可以为相关产业提供全新的认知。本研究的研究结果表明行业从业者可以根据顾客网络评论来制定相应的营销策略。

本研究利用网络顾客评论及相关分析方法, 了解顾客咖啡店体验及满意度。本研究同时利用文本评论和数字评论来挖掘和分析咖啡店顾客的定性感知和定量数据信息。

关键词咖啡店 在线顾客评论 文本挖掘 语义网络分析 经营类型

文章类型： 研究型论文

Este estudio intenta explorar las relaciones encubiertas de palabras, mediante el análisis de redes semánticas, pero aún más, identificar los factores destacados que explican la satisfacción de los clientes de las cafeterías a través del análisis de reseñas online y, por último, examinar el efecto moderador de los tipos de negocios de cafeterías en la satisfacción al cliente.

En esta investigación se adoptaron dos procedimientos principales de análisis de “Big Data” en la industria hotelera, uno es la recopilación de datos y el otro es el análisis de datos. En términos de análisis de datos, se efectuó un análisis de frecuencia con minado de texto (text mining), análisis de red semántica, análisis CONCOR para agrupamiento y análisis cuantitativo con variables ficticias para extraer nuevas perspectivas de las reseñas de los clientes online, tanto cualitativa como cuantitativamente.

Diferentes factores fueron extraídos de las reseñas de clientes online que contribuyen a la satisfacción o insatisfacción de estos y, entre estos factores, se examinó que el nuevo factor “Evento de Ventas” está significativamente asociado con la satisfacción al cliente. Además, se verificó el efecto moderador de los tipos de negocios entre la relación de “Valor por Dinero” y la satisfacción del cliente, indicando las diferencias entre los clientes de distintos tipos de cafeterías.

El presente estudio amplia la perspectiva de investigación de las cafeterías, al adoptar las reseñas de clientes online a través de un análisis relativo. Se revelaron nuevas dimensiones como “Evento de Ventas” y la categorización detallada de la “Calidad del Café”, “Interior” y “Entorno Físico”, lo que indica que se podría generar una nueva cognición con una nueva fuente de datos y métodos analíticos. Los profesionales de la industria podrían llevar a cabo la toma de decisiones en función de la información obtenida a través de las reseñas en línea.

El presente estudio utilizó reseñas de clientes online para comprender la experiencia y satisfacción de los clientes de las cafeterías a través de un conjunto de métodos analíticos. Las revisiones numéricas y de texto se tomaron en cuenta simultáneamente para revelar la percepción tanto cualitativa como cuantitativa de la información de los clientes de las cafeterías.

Cafetería, Reseñas de clientes online, Minado de texto, Análisis de redes semánticas, Tipos de negocios

Trabajo de investigación

Online hotel ratings, a form of electronic word of mouth (eWOM), are becoming increasingly important to tourism and hospitality management. Using sentiment analysis based on the big data technique, this paper aims to investigate the relationship between customer sentiment and online hotel ratings from the perspective of customers’ motives in the context of eWOM, and to further identify the moderating effects of review characteristics.

The authors first retrieve 273,457 customer-generated reviews from a well-known online travel agency in China using automated data crawlers. Next, they exploit two different sentiment analysis methods to obtain sentiment scores. Finally, empirical studies based on threshold regressions are conducted to establish the asymmetric relationship between customer sentiment and online hotel ratings.

The results suggest that the relationship between customer sentiment and online hotel ratings is asymmetric, and a negative sentiment score will exert a larger decline in online hotel ratings, compared to a positive sentiment score. Meanwhile, the reviewer level and reviews with pictures have moderating effects on the relationship between customer sentiment and online hotel ratings. Moreover, two different types of sentiment scores output by different sentiment analysis methods verify the results of this study.

The moderating effects of reviewer level and reviews with pictures offer new insights for hotel managers to make different customer service policies and for customers to select a hotel based on reviews from the online travel agency.

This paper contributes to the literature by applying big data analysis to the issues in hotel management. Based on the eWOM communication theories, this study extends previous study by providing an analysis framework for the relationship between customer sentiment and online hotel ratings from the perspective of customers’ motives in the context of eWOM.

Based on climate issues and carbon emissions, this study aims to promote low-carbon consumption and compel consumers to actively shift to energy-saving appliances. In this big data era, online reviews in social and electronic commerce (e-commerce) websites contain valuable product information, which can facilitate firm business strategies and consumer comparison shopping. This study is designed to advance existing research on energy-saving refrigerators by incorporating machine learning models in the analysis of online reviews to provide valuable suggestions to e-commerce platform managers and manufacturers to effectively understand the psychological cognition of consumers.

This study proposes an online e-commerce review mining and management strategy model based on “data acquisition and cleaning, data mining and analysis and strategy formation” through multiple machine learning methods, namely, Bayes networks, support vector machine (SVM), latent Dirichlet allocation (LDA) and importance–performance analysis (IPA), to help managers.

Based on a case study of one of the largest e-commerce platforms in China, this study linguistically analyzes 29,216 online reviews of energy-saving refrigerators. Results indicate that the energy-saving refrigerator features that consumers are generally satisfied with are, in sequential order, logistics, function, price, outlook, after-sales service, brand, quality and space. This study also identifies ten topics with 100 keywords by analyzing 18 different refrigerator models. Finally, based on the IPA, this study allocates different priorities to the features and provides suggestions from the perspective of consumers, the government and manufacturers.

In terms of limitations, future research may focus on the following points. First, the topics identified in this study derive from specific points in time and reviews; thus, the topics may change with the text data. A machine learning-based online review analysis platform could be developed in the future to dynamically improve consumer satisfaction. Moreover, given that consumers' needs may change over time, e-commerce platform types and consumer characteristics, such as user profiles, can be incorporated into the model to effectively analyze trends in consumers' perceived dimensions.

This study fills the gap in previous research in this field, which uses small-sample data for qualitative analysis, while integrating management ideas and proposes an online e-commerce review mining and management strategy model based on machine learning methods. Moreover, this study considers how consumers' emotional and thematic preferences for products affect their purchase decision-making from the perspective of their psychological perception and linguistically analyzes online reviews of energy-saving refrigerators using the proposed mining model. Through the improved IPA model, this study provides optimizing strategies to help e-commerce platform managers and manufacturers.

Excavating valuable outlier information of gray privacy products, the purpose of this study takes the online reviews of women’s underwear as an example, explores the outlier characteristics of online commentary data, and analyzes the online consumer behavior of consumers’ gray privacy products.

This research adopts the social network analysis method to analyze online reviews. Based on the online reviews collected from women’s underwear flagship store Victoria’s Secret at Tmall, this study performs word segmentation and word frequency analysis. Using the fuzzy query method, the research builds the corresponding co-word matrix and conducts co-occurrence analysis to summarize the factors affecting consumers’ purchase behavior of female underwear.

Establishing a formal framework of gray privacy products, this paper confirms the commonalities among consumers with respect to their perceptions of gray privacy products, shows that consumers have high privacy concerns about the disclosure or secondary use of personal private information when shopping gray privacy products, and demonstrates the big difference between online reviews of gray privacy products and their consumer descriptions.

The research lays a solid foundation for future research in gray privacy products. The factors identified in this study provide a practical reference for the continuous improvement of gray privacy products and services.

In 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.

Specifically, 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.

An 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.

The 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.

The purpose of this study is to present a systematic review of the online service failure (OSF) literature and conduct an exhaustive analysis of academic research on this emerging research area.

The current study has adopted a structured systematic literature review approach to synthesize and assess the OSF literature. Further, the study uses the Theory-Context-Characteristics-Methodology (TCCM) framework to propose future research directions in the OSF domain.

This systematic review shows that OSF research is still developing and remains mainly incoherent. Further, the study develops a conceptual framework integrating the frequently reported antecedents, mediators, moderator and consequences in the extant literature. This review also synthesizes the theoretical perspectives adopted for this domain.

The study followed specific inclusion and exclusion criteria to shortlist articles. Further, articles published only in the English language were considered. Hence, the findings of this review cannot be generalized to all OSF literature.

This systematic review has classified antecedents into customers' and service providers' roles which will enable online service providers to understand all sets of factors driving OSF. It also synthesizes and presents service recovery strategies and emphasizes the role of online customer support to fix OSF.

The OSF literature is still developing and remains highly incoherent, suggesting that a synthesized review is needed. This study has systematically reviewed and synthesized the OSF literature to study its development over time and proposes a framework which provides a comprehensive understanding of OSF.