Queen Mary University of London
iGGi PG Researcher
Available for placement
Peyman is interested in using his computer science knowledge to support society's well-being. Raised in a family where almost everyone’s work is somehow related to mathematics and its applications, he became passionate about algorithms and combinatorics from an early age. This prompted him to pursue an undergraduate degree in computer engineering with a focus on IT and AI. He also has been reading about psychology and sociology as his favourite avocation since college. This is one of the main motivations for him to join IGGI as he believes games are an excellent medium to help people in different ways (like improving their problem-solving and multi-tasking skills) and the IGGI project he is involved in allows him to not only extract invaluable knowledge from the gamers’ data that can help studios improve their decision making when making sequels or other games, but to also pursue his long-lasting interest in getting involved in projects that aim to analyze gamers data for the betterment of the society.
A description of Peyman's research:
This project aims to dive deep into understanding the perception of the players and critics about the games of a game studio in detail, and to this end, strives to use (and improve) the state-of-the-art techniques in language modelling and Natural Language Processing to build systems that automatize the extraction of their invaluable views and feelings about the works of a game studio. The challenges, however, for developing such a system are immense. Foremost, building systems that are capable of handling and analyzing the nuances of a video game and the feelings the gamers develop towards different aspects of these games require specialized datasets that are currently lacking. Additionally, most current systems that analyze players’ emotions use classical machine learning approaches, and in the age of big data, analyzing the massive source of available data requires algorithms capable of handling large amounts of data like RNNs and Attention Models. By overcoming these challenges and building an effective system for analyzing players’ sentiments, we can help game studios to build games that are more immersive, influential, and enjoyable to play.