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Purpose: The widespread acceptance of various social platforms has increased the number of users posting about various services based on their experiences about the services. Finding out the intended ratings of social media (SM) posts is important for both organizations and prospective users since these posts can help in capturing the user's perspectives. However, unlike merchant websites, the SM posts related to the service-experience cannot be rated unless explicitly mentioned in the comments. Additionally, predicting ratings can also help to build a database using recent comments for testing recommender algorithms in various scenarios. Design/methodology/approach: In this study, the authors have predicted the ratings of SM posts using linear (Naïve Bayes, max-entropy) and non-linear (k-nearest neighbor, k-NN) classifiers utilizing combinations of different features, sentiment scores and emotion scores. Findings: Overall, the results of this study reveal that the non-linear classifier (k-NN classifier) performed better than the linear classifiers (Naïve Bayes, Max-entropy classifier). Results also show an improvement of performance where the classifier was combined with sentiment and emotion scores. Introduction of the feature "factors of importance" or "the latent factors" also show an improvement of the classifier performance. Originality/value: This study provides a new avenue of predicting ratings of SM feeds by the use of machine learning algorithms along with a combination of different features like emotional aspects and latent factors. [ABSTRACT FROM AUTHOR]

Social media message boards have become a critical source of information during mass emergencies/disasters, leading to appropriate human action. The use of platforms like Twitter to share information about unfolding crises and social media adoption by governments for communication has increased interest in developing rounded disaster management strategies. Although scholarly works exist for modeling human-traits as social media usage predictors, seminal works on using social media as a predictor for human behavior are rare. This paper aims to identify pertinent information communicated amidst a disaster to unearth linguistic and thematic features that make tweets popular and attract human involvement. This research is based on the calamities during the last decade in the Indian subcontinent. We apply computational intelligence to identify features that make a tweet popular during a disaster. Our research suggests that Tweet popularity attracting human action in a disaster is affected by communication style over social media. [ABSTRACT FROM AUTHOR]

The aim of this study is to utilise the user-generated content from social media platforms and merchandise websites to explore various values affecting behavioural intention in context of e-Learning services from the consumption-value-theory perspective. This study has utilised a novel mixed-method approach based on natural language processing (NLP) techniques for the both the qualitative and quantitative analysis. This study has used user-generated content of Coursera (an e-Learning service) consisting of online reviews from Coursera-100 k-dataset and tweets about Coursera. Some of the important themes generated from the thematic-based analysis of tweets are 'value addition', 'course content', 'topic cover', 'reliability of course', 'course quality', 'enjoyed course', 'recommend the course', 'value for money', 'facilitator skills', etc. Results of the empirical study reveal that offers and deals, emotional connect, facilitator quality, course reliability, platform innovativeness, and compatibility are important predictors of behavioural intention. This study concludes with the various limitations and future directions. [ABSTRACT FROM AUTHOR]

Purpose: Social media channels provide an avenue for expressing views about different services/products. However, unlike merchandise/company websites (where users can post both reviews and ratings), it is not possible to understand user's ratings for a particular service-related comment on social media unless explicitly mentioned. Predicting ratings can be beneficial for service providers and prospective customers. Additionally, predicting ratings from a user-generated content can help in developing vast data sets for recommender systems utilizing recent data. The aim of this study is to predict ratings more accurately and enhance the performance of sentiment-based predictors by combining it with the emotional content of textual data. Design/methodology/approach: This study had utilized a combination of sentiment and emotion scores to predict the ratings of Twitter posts (3,509 tweets) in three different contexts, namely, online food delivery (OFD) services, online travel agencies (OTAs) and online learning (e-learning). A total of 29,551 reviews were utilized for training and testing purposes. Findings: Results of this study indicate accuracies of 58.34%, 57.84% and 100% in cases of e-learning, OTA and OFD services, respectively. The combination of sentiment and emotion scores showed an increase in accuracies of 19.41%, 27.83% and 40.20% in cases of e-learning, OFD and OTA services, respectively. Practical implications: Understanding the ratings of social media comments can help both service providers as well as prospective customers who do not spend much time reading posts but want to understand the perspectives of others about a particular service/product. Additionally, predicting ratings of social media comments will help to build databases for recommender systems in different contexts. Originality/value: The uniqueness of this study is in utilizing a combination of sentiment and emotion scores to predict the ratings of tweets related to different online services, namely, e-learning OFD and OTAs. [ABSTRACT FROM AUTHOR]

Natural and man‐made disasters potentially cause significant damage and disruption to communities. As population density increases and natural disasters become more extreme, it becomes increasingly important to both manage communications and extract information from communications to be able to mitigate the negative effects of such disasters. The emergence of social media platforms has led to new avenues for the collection and dissemination of information that is either local or global. Although social studies have revealed that social media feeds can improve effective disaster preparedness and recovery, it has been observed that the use of social media can have severe negative consequences through the rapid spread of false information leading to the inappropriate allocation of resources and in extreme cases panic and lawlessness. Rumours and false information are likely to affect appropriate corporate responses as well as, where appropriate, responses of public organizations tasked with appropriately responding to natural and man‐made disasters. The ability to identify instances of false information through the course of natural and man‐made disasters is a critical capability for corporate and public bodies in order to improve disaster management and response. To reduce the impact of these rumours, a technique is proposed that makes use of supervised learning to differentiate between information about an actual event from information about a false one and communicating it effectively to appropriate organizations. For this purpose, 934 social media feeds were analysed using a Naïve Bayes classifier. Clearly, applied early on this technique potentially can improve the quality of disaster response and recovery and mitigate the negative consequences. Broadly speaking, we consider this research to relate to the management of knowledge and information flow in disaster situations. Clearly, the techniques introduced in this paper, in providing individuals and organizations with access to knowledge rather than false information and rumours, will help organizations manage resources and activities during disasters more efficiently and effectively. The study concludes with the implications, limitations, and future directions. [ABSTRACT FROM AUTHOR]