Application of social media analytics: a case of analyzing online hotel reviews

A bibliometric analysis of research on tourism content marketing: Background knowledge and thematic evolution

Content marketing is becoming an important trend in the tourism industry and is attracting the attention of many stakeholders. Previous studies have sporadically highlighted only some content marketing aspects, but none comprehensively described the topic. To fill this gap, this study adopts a comprehensive approach by combining two bibliometric co-citation and co-keyword analysis methods of 659 articles on content marketing in travel sectors. The co-citation results indicate that tourism content marketing research has been concentrated on the themes of (1) the impact of electronic word of mouth (eWOM) and word of mouth (WOM) on business performance; (2) the role of social media, user-generated content (UGC) and destination image formulation; (3) the impact of eWOM and UGC on the decision-making process; as well as (4) opportunities and challenges. Additionally, based on the co-keyword analysis, hot research topics are explored, including online review implementation; UGC implementation; communication and information search; customer behavior prediction model; the decision-making process; and issues related to user experience, quality, and management. Among these, UGC implementation is the most likely trend that researchers can develop in the future. In addition, the influence of other types of UGC (e.g., user-generated travel videos) is a promising avenue for future research. This study will help researchers understand the role and influence of tourism content marketing. Furthermore, tourism marketers can use content marketing to restore destination image and address last-minute booking issues after the COVID-19 pandemic.

Social media analytics in tourism: a review and agenda for future research

Purpose

The significance of social media in our lives is manifold. The tourism sector closely interacts with existing and potential tourists through social media, and therefore, social media analytics (SMA) play a critical role in the uplift of the sector. Hence, this review focus on the role of SMA in tourism as discussed in different studies over a period of time. The purpose of this paper to present the state of the art on social media analytics in tourism.

Design/methodology/approach

The review focuses on identifying different SMA techniques to explore the trends and approaches adopted in the tourism sector. The review is based on 83 papers and discuss the studies related to different social media platforms, the travelers' reactions to a particular place and how the tourism experience is enriched by the way of SMA.

Findings

Findings indicate different sentiments associated with tourism and provides a review of tourists’ use of social media for choosing a travel destination. The various analytical approaches, areas such as social network analysis, content analysis, sentiment analysis and trend analysis were found most prevalent. The theoretical and practical implications of SMA are discussed. The paper made an effort to bridge the gap between different studies in the field of tourism and SMA.

Originality/value

SMA facilitate both tourists and tourism companies to understand the trends, sentiments and desires of tourists. The use of SMA offers value to companies for designing quick and adequate services to tourists.

Prediction of polarities of online hotel reviews: an improved stacked decision tree (ISD) approach

Purpose

There is a need to predict whether the consumers liked the stay in the hotel rooms or not, and to remove the aspects the customers did not like. Many customers leave a review after staying in the hotel. These reviews are mostly given on the website used to book the hotel. These reviews can be considered as a valuable data, which can be analyzed to provide better services in the hotels. The purpose of this study is to use machine learning techniques for analyzing the given data to determine different sentiment polarities of the consumers.

Design/methodology/approach

Reviews given by hotel customers on the Tripadvisor website, which were made available publicly by Kaggle. Out of 10,000 reviews in the data, a sample of 3,000 negative polarity reviews (customers with bad experiences) in the hotel and 3,000 positive polarity reviews (customers with good experiences) in the hotel is taken to prepare data set. The two-stage feature selection was applied, which first involved greedy selection method and then wrapper method to generate 37 most relevant features. An improved stacked decision tree (ISD) classifier) is built, which is further compared with state-of-the-art machine learning algorithms. All the tests are done using R-Studio.

Findings

The results showed that the new model was satisfactory overall with 80.77% accuracy after doing in-depth study with 50–50 split, 80.74% accuracy for 66–34 split and 80.25% accuracy for 80–20 split, when predicting the nature of the customers’ experience in the hotel, i.e. whether they are positive or negative.

Research limitations/implications

The implication of this research is to provide a showcase of how we can predict the polarity of potentially popular reviews. This helps the authors’ perspective to help the hotel industries to take corrective measures for the betterment of business and to promote useful positive reviews. This study also has some limitations like only English reviews are considered. This study was restricted to the data from trip-adviser website; however, a new data may be generated to test the credibility of the model. Only aspect-based sentiment classification is considered in this study.

Originality/value

Stacking machine learning techniques have been proposed. At first, state-of-the-art classifiers are tested on the given data, and then, three best performing classifiers (decision tree C5.0, random forest and support vector machine) are taken to build stack and to create ISD classifier.

The evolution of data analytics through the lens of business cases

Purpose

The present study provides a comprehensive review of the evolution of data analytics using real-world cases. The purpose is to provide a distinct overview of where the phenomenon was derived from, where it currently stands and where it is heading.

Design/methodology/approach

Three case studies were selected to represent three different eras of data analytics: Yesterday (1950s–1990s), Today (2000s–2020s) and Tomorrow (2030s–2050s).

Findings

Rapid changes in information technologies more likely moving us towards a more cyber-physical society, where an increasing number of devices, people and corporations are connected. We can expect the development of a more connected cyber society, open for data exchange than ever before.

Social implications

The analysis of technological trends through the lens of representative real-world cases helps to clarify where data analytics was derived from, where it currently stands and where it is heading towards. The presented case studies accentuate that data analytics is constantly evolving with no signs of stagnation.

Originality/value

As the field of data analytics is constantly evolving, the study of its evolution based on particular studies aims to better understand the paradigm shift in data analytics and the resulting technological advances in the IT business through the representative real-life cases.

Analyzing user-generated content using natural language processing: a case study of public satisfaction with healthcare systems

While user-generated online content (UGC) is increasingly available, public opinion studies are yet to fully exploit the abundance and richness of online data. This study contributes to the practical knowledge of user-generated online content and machine learning techniques that can be used for the analysis of UGC. For this purpose, we explore the potential of user-generated content and present an application of natural language pre-processing, text mining and sentiment analysis to the question of public satisfaction with healthcare systems. Concretely, we analyze 634 online comments reflecting attitudes towards healthcare services in different countries. Our analysis identifies the frequency of topics related to healthcare services in textual content of the comments and attempts to classify and rank national healthcare systems based on the respondents' sentiment scores. In this paper, we describe our approach, summarize our main findings, and compare them with the results from cross-national surveys. Finally, we outline the typical limitations inherent in the analysis of user-generated online content and suggest avenues for future research.

The influence of online customer reviews on customers’ purchase intentions: a cross-cultural study from India and the UK

Purpose

The purpose of this study is to examine the influence of online customer reviews (OCRs) and electronic word-of-mouth (eWOM) on customers’ purchase intention (PUI). This study also investigates the cultural differences between the customers in India and UK as regards the influence of OCR and customers’ PUIs.

Design/methodology/approach

This study has used socialisation theory, theory of reasoned action, congruity theory and expectation value theory, along with the existing literature to develop the conceptual model. The theoretical model has been validated using the PLS-SEM technique on a survey involving 305 and 280 respondents for India and UK, respectively.

Findings

The findings highlight that gender has no effect on UK customers’ PUIs, whereas age and gender have considerable impacts on Indian customers’ PUIs.

Research limitations/implications

The study only examines the cross-cultural difference between a European country (UK) and an Asian country (India). Also, since the sample size is low, the findings did not represent a generic view.

Practical implications

The proposed model has provided important inputs to the organisations to understand consumer behaviour particularly the study would help marketing departments to formulate their marketing strategies regarding OCR and customers’ PUI.

Originality/value

This study is unique in understanding the implications of OCR and their influence on customer purchase decisions of UK customers and India’s customers. This study also helps to understand the impact of age and gender on OCR and PUIs.

Applying sentiment analytics to examine social media crises: a case study of United Airline's crisis in 2017

Purpose

In recent years, social media crises occurred more and more often, which negatively affect the reputations of individuals, businesses and communities. During each crisis, numerous users either participated in online discussion or widely spread crisis-related information to their friends and followers on social media. By applying sentiment analysis to study a social media crisis of airline carriers, the purpose of this research is to help companies take measure against social media crises.

Design/methodology/approach

This study used sentiment analytics to examine a social media crisis related to airline carriers. The arousal, valence, negative, positive and eight emotional sentiments were applied to analyze social media data collected from Twitter.

Findings

This research study found that social media sentiment analysis is useful to monitor public reaction after a social media crisis arises. The sentiment results are able to reflect the development of social media crises quite well. Proper and timely response strategies to a crisis can mitigate the crisis through effective communication with the customers and the public.

Originality/value

This study used the Affective Norms of English Words (ANEW) dictionary to classify the words in social media data and assigned the words with two elements to measure the emotions: valence and arousal. The intensity of the sentiment determines the public reaction to a social media crisis. An opinion-oriented information system is proposed as a solution for resolving a social media crisis in the paper.

Research on the Role of Influencing Factors on Hotel Customer Satisfaction Based on BP Neural Network and Text Mining

With the flourishing development of the hotel industry, the study of customer satisfaction based on online reviews and data has become a new model. In this paper, customer reviews and ratings on Ctrip.com are used, and TF-IDF and K-means algorithms are used to extract and cluster the keywords of reviews texts. Finally, 10 first-level influencing factors of hotel customer satisfaction are determined: epidemic prevention, consumption emotion, convenience, environment, facilities, catering, target group, perceived value, price, and service. Based on backpropagation neural network and weight matrix operation, an influencing factor analysis model of hotel customer satisfaction is constructed to explore the role of these factors. The results show that consumption emotion, perceived value, epidemic prevention, target group, and convenience would significantly affect customer satisfaction, among which epidemic prevention becomes a new factor affecting customer satisfaction. Environment, facilities, catering, and service have relatively little effect on customer satisfaction, while price has the least effect. This study provides a path and method for online reviews of hotel management to improve customer satisfaction and provides a theoretical basis for the study of online reviews of hotels.

A Bibliometric Analysis of Online Reviews Research in Tourism and Hospitality

This paper reviews the literature on online reviews in tourism and hospitality, and presents the current state of research in the area. A bibliometric approach was used to analyze 632 journal articles on online reviews in tourism and hospitality from 2005 to 2019 from the Scopus Database. This study identifies the most prolific journals, foundational works, and major research themes in the research area. In addition, we analyzed some dimensions of their network structure and the thematic evolution of the research area. The bibliometric method is quantitative and objective, and we carry out an analysis of the area based on citations and keywords. Researchers and business managers can gain useful insights on the current state of the art in this area. There have been only a few literature reviews tracking the growth in this research area, and even fewer using bibliometric methods or science maps. Therefore, this work provides an updated review of this fast-growing area with a bibliometric approach to highlight the recent developments with the aid of science maps, and shows the thematic network structure and evolution with an innovative visualization.

Assessing advertisement quality on C2C social commerce platforms: an information quality approach using text mining

Purpose

The purpose of this paper is to test relevance of the information quality (IQ) framework in understanding quality of advertisements (ads) posted by ordinary consumers.

Design/methodology/approach

The main objective of this study is to assess quality ads posted on customer-to-customer (C2C) social commerce platforms from an IQ framework. The authors deployed innovative text mining techniques to generate features from the IQ framework and then used a machine learning (ML) algorithm to classify ads into three categories ‐ high quality, medium quality and low quality.

Findings

The results show that not all dimensions of IQ framework are important to assess quality of ads posted on the platforms. Potential buyers on these platforms look for appropriate amount of information, which is objective, concise and complete, to make a potential purchase decision.

Research limitations/implications

As the research focuses on specific product categories, it lacks generalisability. Therefore, it needs to be tested for other product categories.

Practical implications

The paper includes recommendation for C2C marketplaces on how to increase quality of ads posted by consumers on the platform.

Originality/value

This study has focused on the user-generated content posted by ordinary consumers on the C2C commerce platform to sell used goods. Though C2C model has been developed on ads posted on C2C platforms, it can be established for brands as it provides them with an insight into latent dimensions that a consumer shall look for in an ad on social commerce platforms.

Public opinion information dissemination in mobile social networks – taking Sina Weibo as an example

Purpose

Social media, especially microblog, has become one of the most popular platforms for public opinion dissemination. However, so far few studies have been conducted to explore information dissemination under the mobile environment. This paper aims to introduce the approach to analyze the public opinion information dissemination in mobile social networks.

Design/methodology/approach

This paper chooses “network attack” as the research topic and extracts 23,567 relevant messages from Sina Microblogs to study the structure of nodes for public opinion dissemination and the characteristics of propagation paths on mobile internet. Public opinion dissemination is compared on both mobile and non-mobile terminals.

Findings

The results reveal the characteristics of public opinion dissemination in mobile environment and identify three patterns of information propagation path. This study concludes that public opinion on mobile internet propagates more widely and efficiently and generates more impact than that on the non-mobile internet.

Social implications

The methods used in this study can be useful for the government and other organizations to analyze and identify problems in online information dissemination.

Originality/value

This paper explores the mechanism of public opinion dissemination on mobile internet in China and further investigates how to improve public opinion management through a case study related to “network attack.”

Vote or not? How various information cues affect helpfulness voting of online reviews

Purpose

The helpfulness vote is a type of aggregate user representation that, by measuring the quality of an online review based on certain criteria, can allow readers to find helpful reviews more quickly. Although widely applied in practice, the effectiveness of the voting mechanism is unsatisfactory. This paper uses the heuristic–systematic model and the theory of dynamics of reviews to shed light on the effect of various information cues (product ratings, word count and product attributes in the textual content of reviews) on online reviews’ aggregative voting process. It proposes a conceptual model of seven empirically tested hypotheses.

Design/methodology/approach

A dataset of user-generated online hotel reviews (n = 6,099) was automatically extracted from Ctrip.com. In order to measure the variable of product attributes as a systematic cue, the paper uses Chinese word segmentation, a part-of-speech tag and word frequency statistics to analyze online textual content. To verify the seven hypotheses, SPSS 17.0 was used to perform multiple linear regression.

Findings

The results show that the aggregative process of helpfulness voting can be divided into two stages, initial and cumulative voting, depending on whether voting is affected by the previous votes. Heuristic (product ratings, word count) and systematic cues (product attributes in the textual content) respectively exert a greater impact on the two stages. Furthermore, the interaction of heuristic and systematic cues plays an important role in both stages, with a stronger impact on the cumulative voting stage and a weaker one on the initial stage.

Practical implications

This paper’s findings can be used to explore improvements to helpfulness voting by aligning it with an individual’s information process strategy, such as by providing more explicating heuristic cues, developing different methods of presenting relevant cues to promote the voting decision at different stages, and specifying the cognitive mechanisms when designing the functions and features of helpfulness voting.

Originality/value

This study explores the aggregative process of helpfulness votes, drawing on the study of the dynamics of online reviews for the first time. It also contributes to the understanding of the influence of various information cues on the process from an information process perspective.

A Review of Text Corpus-Based Tourism Big Data Mining

With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years.

OpinionML—Opinion Markup Language for Sentiment Representation

It is the age of the social web, where people express themselves by giving their opinions about various issues, from their personal life to the world’s political issues. This process generates a lot of opinion data on the web that can be processed for valuable information, and therefore, semantic annotation of opinions becomes an important task. Unfortunately, existing opinion annotation schemes have failed to satisfy annotation challenges and cannot even adhere to the basic definition of opinion. Opinion holders, topical features and temporal expressions are major components of an opinion that remain ignored in existing annotation schemes. In this work, we propose OpinionML, a new Markup Language, that aims to compensate for the issues that existing typical opinion markup languages fail to resolve. We present a detailed discussion about existing annotation schemes and their associated problems. We argue that OpinionML is more robust, flexible and easier for annotating opinion data. Its modular approach while implementing a logical model provides us with a flexible and easier model of annotation. OpinionML can be considered a step towards “information symmetry”. It is an effort for consistent sentiment annotations across the research community. We perform experiments to prove robustness of the proposed OpinionML and the results demonstrate its capability of retrieving significant components of opinion segments. We also propose OpinionML ontology in an effort to make OpinionML more inter-operable. The ontology proposed is more complete than existing opinion ontologies like Marl and Onyx. A comprehensive comparison of the proposed ontology with existing sentiment ontologies Marl and Onyx proves its worth.

Applying big data analytics to support Kansei engineering for hotel service development

Purpose

Leisure and tourism activities have proliferated and become important parts of modern life, and the hotel industry plays a necessary role in the supply for and demand from consumers. The purpose of this paper is to develop guidelines for hotel service development by applying a service development approach integrating Kansei engineering and text mining.

Design/methodology/approach

The online reviews represent the voice of customers regarding the products and services. Consumers’ online comments might become a key factor for consumers choosing hotels when planning their tourism itinerary. With the framework of Kansei engineering, this paper adopts text mining to extract the sets of Kansei words and hotel service characteristics from the online contents as well as the relationships among Kansei words, service characteristics and these two sets. The relationships are generated by using link analysis, and then the guidelines for hotel service development are proposed based on the obtained relationships.

Findings

The results of the present research can provide the hotel industry a comprehensive understanding of hotels’ customers opinions, and can offer specific advice on how to differentiate one’s products and services from competitors’ in order to improve customer satisfaction and increase hotels’ performance in the end. Finally, this study finds out the service development guidelines to meet customers’ requirements which can provide suggestions for hotel managers. The implications both for academic and industry are also drawn based on the obtained results.

Originality/value

Now, in the internet era, consumers can comment on their hotel living experience directly through the internet. The large amount of user-generated content (UGC) provided by consumers also provides chances for the hospitality industry to understand consumers’ opinions through online review mining. The UGC with consumers’ opinions to hotel services can be continuously collected and analyzed by hoteliers. Therefore, this paper demonstrates how to apply the hybrid approach integrating Kansei engineering and online review mining to hotel service development.