Our framework is applicable to other sequence classification problems irrespective to the size of the datasets. This paper works with code-mixed YouTube comments for Tamil, Malayalam, and Kannada languages. The selection of these tasks is motivated by the lack of large labelled data for user-generated code-mixed datasets. Sentiment analysis and offensive language identification share similar discourse properties. It is challenging to obtain extensive annotated data for under-resourced languages, so we investigate whether it is beneficial to train models using multi-task learning. Building on the qualitative findings, we present design guidelines to aid the adoption of asymmetric VR in the industrial context. The expert user study results demonstrate the usefulness and relevance of asymmetric VR to improve remote training sessions and other application industrial scenarios, while the “Research Panel” data provided detailed insight into the session flow. We also introduce the “Research Panel” tool to gather reactions of learners during the remote training session. To evaluate this approach, we conducted a remote user study with ten industrial maintenance and installation experts. The VR in this case can be seen as a source of visual and other contextual information to advance the effects of situated learning and enhance knowledge transfer. In this article, we investigate the asymmetric use of a VR training solution-among devices with different levels of immersion and control-to enrich the content of remote training sessions.
#Emoticons for angry face for facebook full
However, COVID-19 pandemic restrictions made it impossible for VR training centers to operate on a full scale, forcing traditional face-to-face learning sessions to become remote. Training in virtual reality (VR) is a valuable supplementing tool for advancing knowledge transfer that results in increased efficiency and accuracy of technicians in fieldwork. Some strengths and limitations of the study are discussed as well. Despite contextual relevance, we presume that in socially and morally unacceptable events like rape and war, the valences of reactions alter to some extent: angry and sad usually become positive, while love, wow, and haha become negative. Although many users tend to mock and laugh at rape incidents and the victims, trend lines suggest that such expressions may not be consistent with time. In rape news, both reactions are consistent and maintain a strong positive correlation, meaning they increase and decrease together. Based on the theories of emotion, we quantitatively answer one research question: How do social media users react to rape with the five major Facebook reactions? The results suggest that users are more likely to express disdain toward rape and sympathy toward the victims using the angry button, along with the sad button. The primary aim of this study was to understand users’ different reaction patterns based on the five major Facebook reactions (i.e., love, haha, wow, sad, and angry). This study investigated 3.50 million Facebook reactions collected from 9,429 Bangladeshi news items about rape shared on social media from 2016 to 2021.
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