TY - GEN
T1 - Online multiscale-data classification based on multikernel adaptive filtering with application to sentiment analysis
AU - Iwamoto, Ran
AU - Yukawa, Masahiro
N1 - Funding Information:
This work was supported by KAKENHI 18H01446, 15H02757.
Funding Information:
This work was supported by KAKENHI 18H01446, 15H02757. The first author would like to thank Dr. H. Watanabe of IBM Research - Tokyo, Profs. K. Ohara and H. Saito of Keio University for sincere encouragements as well as fruitful discussions on the future scopes.
Publisher Copyright:
© 2019 IEEE
PY - 2019/9
Y1 - 2019/9
N2 - We present an online method for multiscale data classification, using the multikernel adaptive filtering framework. The target application is Twitter sentiment analysis, which is a notoriously challenging task of natural language processing. This is because (i) each tweet is typically short, and (ii) domain-specific expressions tend to be used. The efficacy of the proposed multiscale online method is studied with dataset of Twitter. Simulation results show that the proposed approach achieves a higher F1 score than the other online-classification methods, and also outperforms the nonlinear support vector machine.
AB - We present an online method for multiscale data classification, using the multikernel adaptive filtering framework. The target application is Twitter sentiment analysis, which is a notoriously challenging task of natural language processing. This is because (i) each tweet is typically short, and (ii) domain-specific expressions tend to be used. The efficacy of the proposed multiscale online method is studied with dataset of Twitter. Simulation results show that the proposed approach achieves a higher F1 score than the other online-classification methods, and also outperforms the nonlinear support vector machine.
KW - Online learning
KW - Reproducing kernel
KW - Sentiment analysis
UR - http://www.scopus.com/inward/record.url?scp=85075615659&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075615659&partnerID=8YFLogxK
U2 - 10.23919/EUSIPCO.2019.8902958
DO - 10.23919/EUSIPCO.2019.8902958
M3 - Conference contribution
AN - SCOPUS:85075615659
T3 - European Signal Processing Conference
BT - EUSIPCO 2019 - 27th European Signal Processing Conference
PB - European Signal Processing Conference, EUSIPCO
T2 - 27th European Signal Processing Conference, EUSIPCO 2019
Y2 - 2 September 2019 through 6 September 2019
ER -