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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  • Cite this

    Iwamoto, R., & Yukawa, M. (2019). Online multiscale-data classification based on multikernel adaptive filtering with application to sentiment analysis. In EUSIPCO 2019 - 27th European Signal Processing Conference (European Signal Processing Conference; Vol. 2019-September). European Signal Processing Conference, EUSIPCO. https://doi.org/10.23919/EUSIPCO.2019.8902958