Measuring service quality and related key dimensions has been an important problem in Marketing. In this research, we would introduce a smart methodological framework to efficiently identify refined, key sentiment dimensions for measuring the service quality using both traditional survey and unstructured online reviews (natural survey). The proposed framework consists of three parts: (1) steps for preprocessing the unstructured reviews to generate attribute-level sentiments for independent variables (2) Bayesian regression to efficiently identify key groups of correlated attributes and (3) post-hoc analysis for identifying dimensions from the selected groups of correlated attributes and predicting dimension-level effects. Note, the first part of the framework (i.e., preprocessing) is not required for analyzing traditional surveys. Our framework provides two sets of complementing results such as attribute-level effects under the identified dimensions and aggregate dimension-level effects. In the first application study to traditional SERVQUAL data, we successfully validated the proposed framework by comparing the results between our framework and three commonly used existing methods of regression, lasso regression, and factor analysis. In the second empirical application study with the online reviews from a major game review website, STEAM platform, we found that our framework provided a significantly reduced number of key dimensions which were surprisingly efficient for predicting and explaining the service quality ratings, compared with the same set of compared methods in the first study plus the topic model. In particular, with reviews of 2,825 games, three key dimensions of Mechanical playability, Fun in fantasy and Money for value were identified, and we also found that the Mechanical playability could be an important driver of game popularity.

This study explored dominant topics about the metaverse discussed in Twitter and the sentiments in each topic in the case of Decentraland using topic modeling and sentiment analysis. The appraisal theory of emotion and motivation theory were used to explain why positive or negative sentiments were expressed toward specific topics. The majority of topics were related to economic benefits such as coins, NFTs, tokens, estate, land, and spaces or socializing with others at specific events. Many of them included predominantly positive sentiments because consumers appraised them as motive consistent. This serves as an important implication for marketers and developers in the metaverse that they need to focus more on these features so that consumers can interact with the motive-consistent features and thus have positive emotions.

As one of the biggest service-oriented industries worldwide, the hotel industry significantly contributes to environmental degradation in several ways. Service marketers, consumers, and policy makers are increasingly aware of the damage that excessive natural resources depletion in the guise of water and energy consumption and CO2 emissions by hotels might bring about to the planet. As a result of the growing global concern about climate change, there has been an increase in consumer demand for environmentally-responsible hospitality options. One such option is green hotels, which are environmentally-conscious hospitality properties that are gaining popularity worldwide at a rapid pace. Consumers who prioritize eco-friendliness are willing to pay a premium for green hotels. However, unlike tangible products, services such as hotel experiences are subjective, emotional, and therefore, difficult to evaluate before actual usage. One of the prominent ways in which consumers assess green hotels’ credibility is through user generated content in the form of reviews.

This study aims to analyse the overall sentiments of online reviews on restaurants in Malaysia using predictive text analytics. As we know in opinion mining, sentiment analysis is a prominent technique in predictive text mining. It is a technique that categorises opinions in unstructured text format into binary classification (ie. good or bad). The authors attempt to go beyond the binary classification by viewing texts as empirical entities derived using the Term Frequency - Inverse Document Frequency (TF-IDF) weighting algorithm. These empirical entities, based on online reviews of restaurants in Malaysia, are then manifested into hypothetically defined constructs closely reflecting their thematic and semantic nature. The were 4914 customer reviews from restaurants across 20 towns and cities in Malaysia scraped off TripAdvisor.com using web crawler tools. Then a series of analytical tests were carried out. First the online reviews were parsed, filtered and clustered using SAS Text Miner. Then the online reviews underwent the TF-IDF process to identify significant terms and weightages were assigned according to their importance. The TF-IDF process resulted in a series of important nouns and adjectives from the text corpus. Using these weightages of nouns and adjectives, the authors went on to thematise these terms based on their semantic nature to manifest hypothetical constructs. These constructs were based on the Mehrabian–Russell Stimulus Response Model. Subsequently the authors tested the associations among the constructs using variance-based and covariance-based Structural Equation Modelling (SEM). The authors were encouraged by this exploratory methodological approach in formulating predictive text analytics using SEM. Results indicated that sentiments were generally positive towards restaurants and the important terms derived were price, hospitality, location, waiting time, availability of parking and size of food portion.

현재까지 모바일 게임 사용자 연구는 개별 콘텐츠의 재미, 중독성, 편의성과 같은 1차적 정서 를 분석하는 차원에 머물러 있다. 그러나 스마트폰의 확산 이후 사용자들의 멀티태스킹이 보편 화되면서 사용자의 게임 콘텐츠 경험은 복잡해지고 있다. 따라서 다양한 행위를 동시에 수행하 는 사용자의 관점에서 모바일 게임에 대한 보다 깊이 있는 분석이 필요한 상황이다. 본 연구는 집단 감성의 관점에서 모바일 게임 사용자들의 연결된 심성 모형을 포착하고자 했다. 이를 위 해 사용자들의 비의도성과 의도성을 동시에 포착할 수 있는 소셜 데이터 분석을 실시했으며, 그 결과로 서비스의 교차 소비, 정보 추천방식의 다양화, 관계 기반의 과제 경험을 주요 이슈로 제시했다.