Authors:
Masato Tokuhisa
;
Jin'ichi Murakami
and
Satrou Ikehara
Affiliation:
Tottori University, Japan
Keyword(s):
Affect, Emotion, Sentence pattern, Sentiment analysis, Web mining.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Knowledge Discovery and Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Natural Language Processing
;
Pattern Recognition
;
Soft Computing
;
Symbolic Systems
;
Web Mining
Abstract:
This paper proposes an affective blog analyzer which can capture people's emotional targets. The existing affective analysis has some problems. For instance, polarity analysis or positive/negative classification for documents are developed, but emotional targets can not be extracted. Some investigations can capture customer's wanted/needed objects, but the knowledge is domain dependent. Therefore, it can not analyze people's everyday life. Against these problems, this paper uses a sentence pattern dictionary to analyze emotions. The dictionary covers Japanese fundamental 6,000 verbs and contains 14,800 patterns with emotional information for everyday life. This dictionary is available for analyzing the downloaded blog articles. After analyzing blogs, many keywords can be extracted as emotional targets. In order to filter and sort them for supporting blog analysts, two parameters are applied. One is Z-score in terms of the frequency of the target appearance, and another is probability
of emotions. In the experiments, trendy and emotional targets were successfully extracted from 6-month-blogs. Thus, the effects of the patterns and parameters are confirmed.
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