Name
Abstract | ||
|---|---|---|
Optimizing player retention and engagement by providing tailored game content to their audience remain as a challenging task for game developers. Tracking and analyzing player engagement data such as in-game behavioral data as well as out-game, such as online text reviews or social media postings, are crucial in identifying user concerns and capturing user preferences. In particular, studying and understanding user reviews has therefore become an integral component of any game development process and is pursued as a research area actively. In this paper, we are interested in extracting latent and influential topics by analyzing text reviews on a popular game community website. Towards addressing this, we present an exploratory analysis with the application of a hierarchical community detection-based hybrid algorithm that extract topics from a given corpus of game reviews. Our analysis reveals interesting topics and sub-topics which can be used for further downstream analysis. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-29516-5_43 | INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 1 |
Keywords | DocType | Volume |
Text analysis, Community detection, Opinion mining | Conference | 1037 |
ISSN | Citations | PageRank |
2194-5357 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maren Pielka | 1 | 0 | 3.04 |
| Rafet Sifa | 2 | 0 | 0.34 |
| Rajkumar Ramamurthy | 3 | 0 | 0.34 |
| Cesar Ojeda | 4 | 0 | 0.34 |
| Christian Bauckhage | 5 | 1979 | 195.86 |