Sentiment classification for unlabeled dataset using Doc2vec with JST

Sangheon Lee, Xiangdan Jin, Wooju Kim

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

5 Citations (Scopus)

Abstract

Supervised learning require sentiment labeled corpus for training. But it is hard to apply automatic sentiment classification system to new domain because labeled dataset construction costs a lot of time. Meanwhile, researches using Doc2vec based document representation beat out other sentiment classification researches. However, these document representation methods only represent documents' context or sentiment. In this paper, we proposed supervised learning scheme for unlabeled corpus and also proposed document representation method which can simultaneously represent documents' context and sentiment.

Original languageEnglish
Title of host publicationProceedings of the 18th Annual International Conference on Electronic Commerce
Subtitle of host publicatione-Commerce in Smart connected World, ICEC 2016
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450342223
DOIs
Publication statusPublished - 2016 Aug 17
Event18th International Conference on Electronic Commerce, ICEC 2016 - Suwon, Korea, Republic of
Duration: 2016 Aug 172016 Aug 19

Publication series

NameACM International Conference Proceeding Series
Volume17-19-August-2016

Other

Other18th International Conference on Electronic Commerce, ICEC 2016
CountryKorea, Republic of
CitySuwon
Period16/8/1716/8/19

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Lee, S., Jin, X., & Kim, W. (2016). Sentiment classification for unlabeled dataset using Doc2vec with JST. In Proceedings of the 18th Annual International Conference on Electronic Commerce: e-Commerce in Smart connected World, ICEC 2016 [2971631] (ACM International Conference Proceeding Series; Vol. 17-19-August-2016). Association for Computing Machinery. https://doi.org/10.1145/2971603.2971631