|
Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
A System for Affect Analysis of Utterances in Japanese Supported with Web Mining
| Title: | A System for Affect Analysis of Utterances in Japanese Supported with Web Mining |
| Authors: | Ptaszynski, Michal Browse this author | | Dybala, Pawel Browse this author | | Shi, Wenhan Browse this author | | Rzepka, Rafal Browse this author →KAKEN DB | | Araki, Kenji Browse this author →KAKEN DB |
| Keywords: | Affect analysis | | Emotiveness | | Analysis of emotiveness | | Web mining | | Evaluation methods |
| Issue Date: | 30-Jun-2009 |
| Publisher: | Japan Society for Fuzzy Theory and Intelligent Informatics |
| Journal Title: | Journal of Japan Society for Fuzzy Theory and Intelligent Informatics |
| Journal Title(alt): | 知能と情報 |
| Volume: | 21 |
| Issue: | 2 |
| Start Page: | 194 |
| End Page: | 213 |
| Publisher DOI: | 10.3156/jsoft.21.194 |
| Abstract: | We propose a method for affect analysis of textual input in Japanese supported with Web mining. The method is based on a pragmatic reasoning that emotional states of a speaker are conveyed by emotional expressions used in emotive utterances. It means that if an emotive expression is used in a sentence in a context described as emotive, the emotion conveyed in the text is revealed by the used emotive expression. The system ML-Ask (Emotive Elements / Expressions Analysis System) is constructed on the basis of this idea. An evaluation of the system is performed in which two evaluation methods are compared. To choose the most objective evaluation method we compare the most popular method in the field and a method proposed by us. The proposed evaluation method was shown to be more objective and revealed the strong and weak points of the system in detail. In the evaluation experiment ML-Ask reached human level in recognizing the general emotiveness of an utterance (0.83 balanced F-score) and 63% of human level in recognizing the specific types of emotions. We support the system with a Web mining technique to improve the performance of emotional state types extraction. In the Web mining technique emotive associations are extracted from the Web using co-occurrences of emotive expressions with morphemes of causality. The Web mining technique improved the performance of the emotional states types extraction to 85% of human performance. |
| Type: | article |
| URI: | http://hdl.handle.net/2115/63602 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
|
Submitter: RZEPKA Rafal
|
