Understanding the Emotions of Those With a Gambling Disorder: Insights From Automated Text Analysis
Keywords:emotions, gambling disorder, automated text analysis
The diffusion and growth of the web and the social media applications that it has provided have seen people increasingly turn to social media to express their feelings, frustrations, and ambitions and to generally share life events. Like other internet users, those with a gambling disorder are also known to use specialized online forums to read the experiences articulated by others and to open up, share, and express themselves online. In this study, we examined how those with a gambling disorder talk about their emotions and express sentiment through their online comments. Sentiment Analysis in IBM Watson was used to capture expressed emotions (anger, disgust, fear, happiness, sadness, and surprise) and sentiments. These data were then used as input to a latent class cluster modelling procedure aimed at categorizing those with a gambling disorder into distinct groups. The findings show how qualitative online data can be transformed into quantitative insights in order to identify different categories of people with a gambling disorder. The technique offers a non-intrusive method of data collection that can provide useful insights into the emotions felt and expressed by those with a gambling disorder.
L’usage répandu d’applications Internet et de médias sociaux a conduit les gens à se tourner de plus en plus vers les réseaux sociaux pour communiquer leurs sentiments, leurs ambitions et ce qui se passe dans leur vie. À l’instar d’autres internautes, les personnes qui ont des problèmes de jeu fréquentent des forums spécialisés en ligne pour s’exprimer et savoir ce que vivent des gens ayant des problèmes semblables aux leurs. Cette étude examine, à travers leurs commentaires en ligne, comment ces personnes aux prises avec une dépendance au jeu parlent de leurs émotions. Nous nous servons de la fonction d’analyse des sentiments d’IBM Watson pour capturer les émotions exprimées (colère, aversion, peur, joie, tristesse et étonnement) et autres sentiments. Ces données font ensuite l’objet d’une modélisation conjuguant l’analyse des classes latentes et l’analyse par grappe, et qui vise à constituer des sous-groupes de joueurs ayant un problème de dépendance. Les résultats font voir que les données qualitatives en ligne peuvent être transformées en données quantitatives qui permettent de distinguer des sous-groupes parmi les personnes aux prises avec une dépendance au jeu. Nous traitons dans cet article de la possibilité de traitements ciblés à l’intention de ces sous-groupes, ainsi que des limites de cette méthode.
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