Online multiscale-data classification based on multikernel adaptive filtering with application to sentiment analysis

Ran Iwamoto, Masahiro Yukawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationEUSIPCO 2019 - 27th European Signal Processing Conference
PublisherEuropean Signal Processing Conference, EUSIPCO
ISBN (Electronic)9789082797039
DOIs
Publication statusPublished - 2019 Sep
Event27th European Signal Processing Conference, EUSIPCO 2019 - A Coruna, Spain
Duration: 2019 Sep 22019 Sep 6

Publication series

NameEuropean Signal Processing Conference
Volume2019-September
ISSN (Print)2219-5491

Conference

Conference27th European Signal Processing Conference, EUSIPCO 2019
CountrySpain
CityA Coruna
Period19/9/219/9/6

Keywords

  • Online learning
  • Reproducing kernel
  • Sentiment analysis

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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