TY - GEN
T1 - Document-level sentiment classification in japanese by stem-based segmentation with category and data-source information
AU - Bao, Siya
AU - Togawa, Nozomu
N1 - Funding Information:
This paper is supported in part by JST CREST (Grant No. JPMJCR19K4)
Funding Information:
Acknowledgement: This paper is supported in part by JST CREST (Grant No. JPMJCR19K4).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - Existing studies focus on text information while ignoring category and data source information, both of which are verified to be important in interpreting sentiments in travel comments in this paper. Furthermore, the unique linguistic characteristics of Japanese cause difficulty in applying the conventional token-based word segmentation methods to Japanese comments directly. In this paper, we propose a method of stem-based segmentation based on Japanese linguistic characteristics and incorporate it with category and data source information into a hierarchical network model for document-level sentiment classification. Empirical results of our proposed model outperform existing models on a real-world dataset.
AB - Existing studies focus on text information while ignoring category and data source information, both of which are verified to be important in interpreting sentiments in travel comments in this paper. Furthermore, the unique linguistic characteristics of Japanese cause difficulty in applying the conventional token-based word segmentation methods to Japanese comments directly. In this paper, we propose a method of stem-based segmentation based on Japanese linguistic characteristics and incorporate it with category and data source information into a hierarchical network model for document-level sentiment classification. Empirical results of our proposed model outperform existing models on a real-world dataset.
KW - Category and data-source information
KW - Document-level sentiment classification
KW - Japanese
KW - Stem-based segmentation
UR - http://www.scopus.com/inward/record.url?scp=85083450670&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083450670&partnerID=8YFLogxK
U2 - 10.1109/ICSC.2020.00062
DO - 10.1109/ICSC.2020.00062
M3 - Conference contribution
AN - SCOPUS:85083450670
T3 - Proceedings - 14th IEEE International Conference on Semantic Computing, ICSC 2020
SP - 311
EP - 314
BT - Proceedings - 14th IEEE International Conference on Semantic Computing, ICSC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE International Conference on Semantic Computing, ICSC 2020
Y2 - 3 February 2020 through 5 February 2020
ER -