Fundamental Technologies of Virtual Communities Creating Game Mining

网络游戏作为一种新的文化、社会和经济模式，近年来引起了人们的极大兴趣。从游戏运营的角度来看，在虚拟世界中促进建设性活动是至关重要的，因为这样做会增加对游戏的依恋，因为在游戏中有一个快乐的生活。因此，需要对游戏内外的用户趋势进行把握，为了把握用户趋势，有两种可能的途径，即，动作分析处理游戏或移动风格的模式，以及通信分析处理聊天或公告板内容。在本研究中，采用了后一种方法。更准确地说，针对游戏内的聊天、游戏外的公告栏和博客，我们提出了"关键词提取"、"讨论可视化"和"社区可视化"的方法，并使用真实的数据对它们进行了评估。在关键词提取方面，我们提出了一种新的方法，用于从公告栏服务中的文本中检测有影响力的关键词。在讨论可视化中，我们提出了一种基于KeyGraph的词间关系表示工具的预处理方法。所提出的预处理方法根据其重要性划分评论。在社区可视化中，我们提出了一种通过关注聊天消息中的常用词来可视化用户网络的方法。我们评估了所提出的方法与真实的数据从聊天室和聊天数据在客户端的在线游戏。我们还提出了一种方法来可视化的公告板服务中的用户使用森林隐喻，可以有效地表示重要的用户，回复数量，重要的话，等等。

Online games, each constituting a virtual community with more than ten thousand participants, have recently gained a lot of interests as a new cultural, social, and economical model. From a game operating point of view, it is crucial to promote constructive activities in the virtual world because doing so increases attachment to the game due to having a happy life therein. Accordingly, grasping user trends both inside and outside of the game is necessary.To grasp user trends, there exist two possible approaches, i.e., action analysis dealing with patterns of playing or moving styles, and communication analysis dealing with chat or bulletin board contents. In this research, the latter approach was adopted. More precisely, targeting at chats inside the game, bulletin boards and blogs outside the game, we proposed methods for "keyword extraction", "discussion visualization", and "community visualization", and we evaluated them using real data.In keyword extraction, we proposed a new method for detection of influential keywords from texts in bulletin board services. The proposed method considers additionally the relationship between comments and their replies as an index of important words.In discussion visualization, we proposed a preprocessing method for a tool called KeyGraph that can express the relationship between words. The proposed preprocessing method partitions comments according to their importance. This preprocessing successfully enables grasping of discussions in more detail.In community visualization, we proposed a method for visualizing user networks by focusing on their common words in chat messages. We evaluated the proposed method with real data from a chat room and chat data at the client side of an online game. We also proposed a method for visualizing users in a bulletin board service by using forest metaphor that can effectively represent important users, number of replies, important words, etc.