Dataset Information

Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis.

ABSTRACT: In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs). Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it.

SUBMITTER: Kim YB

PROVIDER: S-EPMC4524693 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis.

Kim Young Bin YB Lee Sang Hyeok SH Kang Shin Jin SJ Choi Myung Jin MJ Lee Jung J Kim Chang Hun CH

PloS one 20150804 8

In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs). Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To addr ...[more]

PMID: 26241496

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Project description:BackgroundMental health in general, and depression in particular, remain undertreated conditions. Mobile health (mHealth) apps offer tremendous potential to overcome the barriers to accessing mental health care and millions of depression apps have been installed and used. However, little is known about the effect of these apps on a potentially vulnerable user population and the emotional reactions that they generate, even though emotions are a key component of mental health. App reviews, spontaneously posted by the users on app stores, offer up-to-date insights into the experiences and emotions of this population and are increasingly decisive in influencing mHealth app adoption.ObjectiveThis study aims to investigate the emotional reactions of depression app users to different app features by systematically analyzing the sentiments expressed in app reviews.MethodsWe extracted 3261 user reviews of depression apps. The 61 corresponding apps were categorized by the features they offered (psychoeducation, medical assessment, therapeutic treatment, supportive resources, and entertainment). We then produced word clouds by features and analyzed the reviews using the Linguistic Inquiry Word Count 2015 (Pennebaker Conglomerates, Inc), a lexicon-based natural language analytical tool that analyzes the lexicons used and the valence of a text in 4 dimensions (authenticity, clout, analytic, and tone). We compared the language patterns associated with the different features of the underlying apps.ResultsThe analysis highlighted significant differences in the sentiments expressed for the different features offered. Psychoeducation apps exhibited more clout but less authenticity (ie, personal disclosure). Medical assessment apps stood out for the strong negative emotions and the relatively negative ratings that they generated. Therapeutic treatment app features generated more positive emotions, even though user feedback tended to be less authentic but more analytical (ie, more factual). Supportive resources (connecting users to physical services and people) and entertainment apps also generated fewer negative emotions and less anxiety.ConclusionsDevelopers should be careful in selecting the features they offer in their depression apps. Medical assessment features may be riskier as users receive potentially disturbing feedback on their condition and may react with strong negative emotions. In contrast, offering information, contacts, or even games may be safer starting points to engage people with depression at a distance. We highlight the necessity to differentiate how mHealth apps are assessed and vetted based on the features they offer. Methodologically, this study points to novel ways to investigate the impact of mHealth apps and app features on people with mental health issues. mHealth apps exist in a rapidly changing ecosystem that is driven by user satisfaction and adoption decisions. As such, user perceptions are essential and must be monitored to ensure adoption and avoid harm to a fragile population that may not benefit from traditional health care resources.

Project description:We created a virtual reality version of a 1983 performance by Dire Straits, this being a highly complex scenario consisting of both the virtual band performance and the appearance and behaviour of the virtual audience surrounding the participants. Our goal was to understand the responses of participants, and to learn how this type of scenario might be improved for later reconstructions of other concerts. To understand the responses of participants we carried out two studies which used sentiment analysis of texts written by the participants. Study 1 (n = 25) (Beacco et al. in IEEE Virtual Reality: 538-545, 2021) had the unexpected finding that negative sentiment was caused by the virtual audience, where e.g. some participants were fearful of being harassed by audience members. In Study 2 (n = 26) notwithstanding some changes, the audience again led to negative sentiment-e.g. a feeling of being stared at. For Study 2 we compared sentiment with questionnaire scores, finding that the illusion of being at the concert was associated with positive sentiment for males but negative for females. Overall, we found sentiment was dominated by responses to the audience rather than the band. Participants had been placed in an unusual situation, being alone at a concert, surrounded by strangers, who seemed to pose a social threat for some of them. We relate our findings to the concept of Plausibility, the illusion that events and situations in the VR are really happening. The results indicate high Plausibility, since the negative sentiment, for example in response to being started at, only makes sense if the events are experienced as actually happening. We conclude with the need for co-design of VR scenarios, and the use of sentiment analysis in this process, rather than sole reliance on concepts proposed by researchers, typically expressed through questionnaires, which may not reflect the experiences of participants.Supplementary informationThe online version contains supplementary material available at 10.1007/s10055-022-00685-9.

Dataset Information

Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis.

Publications

Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis.

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