Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Expressing emotion in text-based communication
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Whose thumb is it anyway?: classifying author personality from weblog text
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Emotion rating from short blog texts
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Using linguistic cues for the automatic recognition of personality in conversation and text
Journal of Artificial Intelligence Research
Identifying expressions of opinion in context
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Reading between the lines: linguistic cues to deception in online dating profiles
Proceedings of the 2010 ACM conference on Computer supported cooperative work
Enhancing collaborative filtering systems with personality information
Proceedings of the fifth ACM conference on Recommender systems
Learning how to feel again: towards affective workplace presence and communication technologies
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Creating and interpreting abstract visualizations of emotion
Proceedings of Graphics Interface 2012
Method for extraction of characteristics of personal characters from life log
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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In this paper, we investigate personality estimation from Japanese weblog text. Among various personality types, we focus on Egogram, which has been used in Transactional Analysis and is strongly related to the communicative behavior of individuals. Estimation is performed using the Multinomial Naïve Bayes classifier with some feature words that are selected based on the information gain. The validity of this approach was evaluated with real weblog text of 551 subjects. The results show that our approach achieved 12-25% improvement from baseline. The feature words selected for the estimation are strongly correlated with the characteristics of Egogram.