Mining Online Game Social Intermedia
Date Issued
2015
Date
2015
Author(s)
Chang, Tsung-Hao
Abstract
As online games have become more and more popular recently, more and more players discuss and share their opinions on online game social media. The contents of media are informative and helpful to game companies and players, but they may be overwhelming and contain plenty of noises. Moreover, every social media may provide different aspects of information. Therefore, in this thesis, we propose a framework to cluster the contents in the forum and the Facebook page of an online game into several topics, analyze the evolution of each topic and sentiment distribution, and compare the topics clustered on both media to provide helpful insights for game companies and players. The proposed framework contains four phases. First, we extract game terms from threads to form a virtual document, where a thread contains a question and a sequence of comments about the question. Second, we modify Latent Dirichlet Allocation (LDA) to cluster the virtual documents into latent topics by increasing the importance of co-occurred game terms. For each topic, we further divide it into several sub-topics by considering the timestamp of each game term in the topic. Third, we compute the sentiment distribution of each topic and thread, and find the outliers. Finally, we analyze similar topics found from both social media, which can provide different insights for game companies. The experiment result shows that the proposed framework can cluster similar and relevant game terms together. The topic evolution, sentiment analysis and intermedia analysis can help game companies understand the demand of players, and help players to know the guidelines and tips of playing the game.
Subjects
online game
multiple social media
LDA
data mining
Type
thesis
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