An opinion mining methodology to analyse games for health

Abstract

Despite 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. It is therefore important to understand how to sustain interest and, in this way, preserve the health benefits of games for health. 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. With the growth of social media, user opinions became widely available in public forums. 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. 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). 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. 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. The methodology proposed builds upon over 500,000 user comments collected from over 32,000 videos.

An Analysis of the Evolution of Public Sentiment and Spatio-Temporal Dynamics Regarding Building Collapse Accidents Based on Sina Weibo Data

“…From a research perspective, current studies on public opinion analysis mainly involve four aspects. Firstly, text mining [6] is the central field of research, encompassing topic mining [7], sentiment analysis [8][9][10][11][12][13], and spatio-temporal analysis [4,5,14]. Secondly, there is user behavior analysis [15].…”