The aim of this research is to develop a search system for comics based on the personalities of appearing characters. For this purpose, this paper describes the classification of characters using egograms, which are used to classify personalities. In the proposed method, texts that express a comic book character's personality are acquired from web resources, and semantic vectors are allocated based on these texts using egograms. The resulting egogram pattern is used to estimate typical properties. Our experiment reveals that the performance accuracy of this classification method is 55.0%.
This paper proposes the following methods to search VOCALOID creators who publish music videos in Niconico video hosting service. For VOCALOID creator search, the user can utilize three clues: VOCALOID character name, music genre, and impressions. We defined the music genre by extending generic digital music genre with considering social tags annotated on VOCALOID music videos. We also implemented SVM-based music impression estimator utilizing viewer comments being over 0.8 points in F-values. We compared the proposal with three comparison methods in 12 search tasks and clarified the effectiveness of music genres and impressions.
With the recent spread of communication using social media, exchanging opinions each other on web has become more common irrespective of age and sex. On the other hand, a problem called as “Internet flaming” often occurs along with the increase of social network service users. One of the reasons might be that the users do not recognize meanings/intentions/emotions expressed by other users’ words. In this study, we focused on slangs (Internet slangs) that are often used on SNS but are not registered in dictionaries, then tried to convert them into standard words. We also intended to output more appropriate candidates by considering not only semantic similarity but also affective similarity. The proposed method conducts filtering and re-ranking over the semantically similar candidates obtained based on distributed representations to detect the inappropriate candidates as standard word by focusing on two points: (1) features of slang/standard word and (2) affective similarity between the inputted word and the candidate words. In the evaluation experiment, the proposed method obtained a higher MRR than the baseline method.