IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conversation and Community: Chat in a Virtual World
Conversation and Community: Chat in a Virtual World
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Project massive: a study of online gaming communities
CHI '04 Extended Abstracts on Human Factors in Computing Systems
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Modeling the Personality of Participants During Group Interactions
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Networks: An Introduction
Introverted elves & conscientious gnomes: the expression of personality in world of warcraft
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Reliable personality prediction can have direct impact on many adaptive systems, such as targeted advertising, interface personalization and content customization. We propose an algorithm to infer a user's personality profile more reliably by fusing analytical predictions from multiple sources including behavioral traces, textual data, and social networking information. We applied and validated our approach using a real data set obtained from 1,040 World of Warcraft players. Besides behavioral and social networking information, we found that text analysis of character names yields the strongest personality cues.