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Title:
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SEMANTIC CLASSIFICATION OF TEXT MESSAGES USING THE CONCEPT OF COMMUNITY IN SOCIAL NETWORK ANALYSIS |
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Author(s):
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Hideya Matsukawa, Yoshiko Arai, Chiaki Iwasaki, Yoko Kinjo, Hiroshi Hotta |
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ISBN:
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978-989-8533-32-6 |
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Editors:
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Piet Kommers and Pedro Isaías |
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Year:
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2015 |
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Edition:
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Single |
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Keywords:
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Social Network Analysis, Community, Connection Component, Semantic Classification, Overview of Messages. |
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Type:
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Poster/Demonstration |
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First Page:
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340 |
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Last Page:
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342 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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In this study, we attempted to classify the massive amount of text data written in a BBS based on the extent of co-occurrence of words within each message. The concept of community in social network analysis was used for classification, and through a simulated annealing algorithm, the community and connection component to which each word belonged was identified. As a result, the semantic consistency in each connection component and community was established to a certain extent. |
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