Browsing by Author "Williams, Salla"

Hostility in the player communication of video games (and by extension, mobile games) is a well-documented phenomenon that can have negative repercussions for the well-being of the individual being subjected to it, and the society in general. Existing research on detecting hostility in games through machine learning methods is scarce due to the unavailability of data, imbalanced existing data (few positive samples in a large data set), and the challenges involved in defining and identifying hostile communication. This thesis utilizes communication data from the Supercell game Brawl Stars to produce two distinct machine learning models: a support vector classifier and a multi-layer perceptron. Their performance is compared to each other as well as to that of an existing sentiment analysis classifier, VADER. Techniques such as oversampling and using additional data are also used in an attempt to reach better results by improving the balance of the data set. The support vector classifier model was found to have the best performance overall, with an F1 score of 64.15% when used on the pure data set and 65.74% when combined with the SMOTE oversampling algorithm. The thesis includes an appendix with a list of the words that were found to have the strongest influence on the hostile/non-hostile classification.