![]() ![]() The results of the information extraction and processing are displayed on a dashboard that allows for the exploration and browsing of the results, which can be useful to better understand the opinions and impacts perceived by users and to inform the (re)design of games for health. In doing so, the methodology uses a pre-established vocabulary, based on the English lexicon and its semantic relations, to annotate the presence of 38 concepts (five of Usability, 18 of UX, and 15 of H-QoL) and to analyse sentiment. This paper introduces a methodology that analyses user comments posted on YouTube about the Just Dance game, to automatically extract information about Usability, User Experience (UX), and Perceived Health Impacts related to Quality of Life (H-QoL). This abundance of information affords us the possibility of, through the application of natural language processing and sentiment analysis techniques, tapping into user opinions and automatically analysing and extracting knowledge from them. With the growth of social media, user opinions became widely available in public forums. This could be achieved by continuously reviewing user feedback after product launch and using this information to inform (re)design and better address user needs. #WHEEL OF EMOTION HOW TO#It is therefore important to understand how to sustain interest and, in this way, preserve the health benefits of games for health. Our proposed-TFICF method showed better performance where the highest accuracy of TFICF is 89%, and the highest accuracy of PMI is 82%.ĭespite the positive impact of games for health on players' health, users tend to stop playing them after a short period of time, leading benefits to fade. The performance of the proposed-TFICF method is analyzed and compared with one of the common methods in this path which is called Pointwise Mutual Information (PMI). The second contribution is that we proposed a new method to automatically generate sentiment lexicons which is called Term Frequency-Inverse Context Frequency (TFICF). The generated resources are publicly available for research purpose. As far as we know, the generated fanatic-lexicons are the first large-scale fanatic-lexicons. The generated fanatic-lexicons can help in building anti-fanatic tools and automatically detecting and measuring sport-fanaticism in Arabic social text. The first contribution is that we generated twelve large-scale fanatic-lexicons that can help in building fanatic-classification to automatically classify Arabic social text (e.g., tweets) into fanatic-text or non-fanatic text. In this paper, two main contributions are introduced. Hence, a huge amount of data is posted on social media every day where text mining and sentiment analysis are essential to automatically analyze such data to extract the desired information and knowledge. Studying this problem in social network sites such as Twitter becomes important where social sites provide a mean for people to communicate and share emotions. Sport-fanaticism is one of the social problems. Finally, we combined sentiment analysis and text mining techniques to discover the relationship between the user polarity and sentiment expressed referring to the different candidates, thus modeling political support of social media users from an emotional viewpoint. We also investigated the temporal dynamics of the online discussions, by studying how users' publishing behavior is related to their political alignment. In this way, we were able to determine in the weeks preceding the election day which candidate or party public opinion is most in favor of. Afterwards, we leveraged a neural-based opinion mining technique for determining the political orientation of social media users by analyzing the posts they published. In particular, we exploited a clustering-based technique for extracting the main discussion topics and monitoring their weekly impact on social media conversation. In this context, the present manuscript provides a precise view of the 2020 US presidential election by jointly applying topic discovery, opinion mining, and emotion analysis techniques on social media data. Every day millions of people use social media platforms by generating a very large amount of opinion-rich data, which can be exploited to extract valuable information about human dynamics and behaviors. ![]()
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