Japanese sentiment analysis using simple alignment sentence classification

Hirotaka Niitsuma, Daiki Kubota, Manabu Ohta

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

Abstract

Recurrent and convolutional neural networks have been used to learn contextual information in many natural-language processing studies. In particular, they are the most successful methods for English-language text analysis. In the sentiment analysis of English-language text, recurrent neural networks with an attention mechanism have been found to perform well. We might assume that context would be less important in Japanese-language sentiment analysis. To examine this assumption, we apply a simple alignment sentence-classification model to Japanese sentiment analysis.

Original languageEnglish
Title of host publicationMEDES 2018 - 10th International Conference on Management of Digital EcoSystems
PublisherAssociation for Computing Machinery, Inc
Pages126-131
Number of pages6
ISBN (Electronic)9781450356220
DOIs
Publication statusPublished - Sep 25 2018
Event10th International Conference on Management of Digital EcoSystems, MEDES 2018 - Tokyo, Japan
Duration: Sep 25 2018Sep 28 2018

Other

Other10th International Conference on Management of Digital EcoSystems, MEDES 2018
CountryJapan
CityTokyo
Period9/25/189/28/18

Keywords

  • Aspect-based sentiment analysis
  • Japanese sentiment analysis
  • Word2vec

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Networks and Communications
  • Environmental Engineering

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    Niitsuma, H., Kubota, D., & Ohta, M. (2018). Japanese sentiment analysis using simple alignment sentence classification. In MEDES 2018 - 10th International Conference on Management of Digital EcoSystems (pp. 126-131). Association for Computing Machinery, Inc. https://doi.org/10.1145/3281375.3281388