2017
Topic extraction on twitter considering author’s role based on bipartite networks
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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- Volume
- 10558
- Number
- First page
- 239
- Last page
- 247
- Language
- English
- Publishing type
- Research paper (international conference proceedings)
- DOI
- 10.1007/978-3-319-67786-6_17
- Publisher
- Springer Verlag
This paper proposes a quality topic extraction on Twitter based on author’s role on bipartite networks. We suppose that author’s role which means who were in what group, affects the quality of extracted topics. Our proposed method expresses relations between authors and words as bipartite networks, explores author’s role by forming clusters using our original community detection technique, and finds quality topics considering the semantic accuracy of words and author’s role.
- Link information
- ID information
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- DOI : 10.1007/978-3-319-67786-6_17
- ISSN : 1611-3349
- ISSN : 0302-9743
- DBLP ID : conf/dis/HashimotoKOS17
- SCOPUS ID : 85030256645