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.

This study investigates the contexts in which emojis occur through co-occurrence, cluster, and association analysis of Airbnb tweets. Findings reveal these positive emojis tend to co-exist with several types of words representing conversation, emotion, activity, and marketing. This study contributes to the textual paralanguage literature in marketing.

This research intends to propose the methodology for analyzing the current trends of agriculture, which directly connects to the survival of the nation, and through this methodology, identify the agricultural trend of Korea. Based on the relationship between three types of data – policy reports, academic articles, and news articles – the research deducts the major issues stored by each data through LDA, the representative topic modeling method. By comparing and analyzing the LDA results deducted from each data source, this study intends to identify the implications regarding the current agricultural trends of Korea. This methodology can be utilized in analyzing industrial trends other than agricultural ones. To go on further, it can also be used as a basic resource for contemplation on potential areas in the future through insight on the current situation. database of the profitability of a total of 180 crop types by analyzing Rural Development Administration’s survey of agricultural products income of 115 crop types, small land profitability index survey of 53 crop types, and Statistics Korea’s survey of production costs of 12 crop types. Furthermore, this research presents the result and developmental process of a web-based crop introduction decision support system that provides overseas cases of new crop introduction support programs, as well as databases of outstanding business success cases of each crop type researched by agricultural institutions.

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.