Extracting video games rating labels from transcript files

Abstract

Video games industry represents one of the most profitable activities connected with entertainment and visual arts. Video game industry involves a great number of different professionals who work together to create products expected to reach people in many countries. A substantial part of these people are teenagers, strongly attracted and influenced by video games. For these reasons, various systems of labels have been created. These systems are based on different criteria, but they have in common the presence of descriptors, or labels, whose indicate the type of contents in the game. These labels give an age range indicator to inform the buyers and users to the more suitable age for the product. One of these systems is called PEGI, and we will primarily take it in consideration for the purposes of our study. The rating procedure includes a large process of manual control of each submitted game. In order to help this large and demanding process, we propose a system of video games rating based on automatic classification of the products performed over the “transcript” or script, files that display the full transcription of dialogues in a video game. The proposed automatic classification algorithm is based on specialized dictionaries enriched with a vector semantics algorithm, and is able to provide an age rating and a genre classification of video games. It works in a more efficient way in games with a consistent amount of dialogues. The experimentation of the proposed algorithm is returning encouraging results.