Kenji Araki

Bio: Kenji Araki is an academic researcher from Hokkaido University. The author has contributed to research in topics: Machine translation & Sentence. The author has an hindex of 20, co-authored 297 publications receiving 1815 citations. Previous affiliations of Kenji Araki include Hokkai Gakuen University & Kitami Institute of Technology.

Towards context aware emotional intelligence in machines: computing contextual appropriateness of affective states

Abstract: This paper presents a novel approach to the estimation of user's affective states in Human-Computer Interaction. Most of the present approaches divide emotions strictly between positive or negative. However, recent discoveries in the field of Emotional Intelligence show that emotions should be rather perceived as context-sensitive engagements with the world. This leads to a need to specify whether the emotions conveyed in a conversation are appropriate for a situation they are expressed in. In the proposed method we use a system for affect analysis on textual input to recognize users emotions and a Web mining technique to verify the contextual appropriateness of those emotions. On this basis a conversational agent can choose to either sympathize with the user or help them manage their emotions. Finally, the results of evaluation of the proposed method with two different conversational agents are discussed, and perspectives for further development of the method are proposed.

CAO: A Fully Automatic Emoticon Analysis System Based on Theory of Kinesics

Abstract: This paper presents CAO, a system for affect analysis of emoticons in Japanese online communication. Emoticons are strings of symbols widely used in text-based online communication to convey user emotions. The presented system extracts emoticons from input and determines the specific emotion types they express with a three-step procedure. First, it matches the extracted emoticons to a predetermined raw emoticon database. The database contains over 10,000 emoticon samples extracted from the Web and annotated automatically. The emoticons for which emotion types could not be determined using only this database, are automatically divided into semantic areas representing “mouths” or “eyes,” based on the idea of kinemes from the theory of kinesics. The areas are automatically annotated according to their co-occurrence in the database. The annotation is first based on the eye-mouth-eye triplet, and if no such triplet is found, all semantic areas are estimated separately. This provides hints about potential groups of expressed emotions, giving the system coverage exceeding 3 million possibilities. The evaluation, performed on both training and test sets, confirmed the system's capability to sufficiently detect and extract any emoticon, analyze its semantic structure, and estimate the potential emotion types expressed. The system achieved nearly ideal scores, outperforming existing emoticon analysis systems.

A SVM-based personal recommendation system for TV programs

Abstract: This paper presents a SVM-based prediction approach for constructing personal recommendation system for TV programs. We have applied support vector machine (SVM) to personal prediction of online Internet electronic program guide (IEPG). Our basic idea is to combine SVM and feedback processing into our system, using user-watched histories as retraining data, to realize personal predictions. We evaluate the precision by experiments with open data. The results show that the proposed polynomial kernel SVM system offers a statistically significant increase in performance compared to other method, and this system demonstrates good dynamically adaptive capability.

A Casual Conversation System Using Modality and Word Associations Retrieved from the Web

Abstract: In this paper we present a textual dialogue system that uses word associations retrieved from the Web to create propositions. We also show experiment results for the role of modality generation. The proposed system automatically extracts sets of words related to a conversation topic set freely by a user. After the extraction process, it generates an utterance, adds a modality and verifies the semantic reliability of the proposed sentence. We evaluate word associations extracted form the Web, and the results of adding modality. Over 80% of the extracted word associations were evaluated as correct. Adding modality improved the system significantly for all evaluation criteria. We also show how our system can be used as a simple and expandable platform for almost any kind of experiment with human-computer textual conversation in Japanese. Two examples with affect analysis and humor generation are given.

Learning Vocabulary In Another Language

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