Adullam at SemEval-2017 Task 4: Sentiment Analyzer based on Lexicon Integrated Convolutional Neural Networks with Attention

Joosung Yoon, Kigon Lyu, Hyeoncheol Kim

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

Abstract

We propose a sentiment analyzer for the prediction of document-level sentiments of English micro-blog messages from Twitter. The proposed method is based on lexicon integrated convolutional neural networks with attention (LCA). Its performance was evaluated using the datasets provided by SemEval competition (Task 4). The proposed sentiment analyzer obtained an average F1 of 55.2%, an average recall of 58.9% and an accuracy of 61.4%.

Original languageEnglish
Title of host publicationACL 2017 - 11th International Workshop on Semantic Evaluations, SemEval 2017, Proceedings of the Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages732-736
Number of pages5
ISBN (Electronic)9781945626555
Publication statusPublished - 2017
Event11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 - Vancouver, Canada
Duration: 2017 Aug 32017 Aug 4

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017
Country/TerritoryCanada
CityVancouver
Period17/8/317/8/4

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2017R1A2B4003558).

Publisher Copyright:
© 2017 Association for Computational Linguistics

ASJC Scopus subject areas

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

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