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 language | English |
---|---|
Title of host publication | ACL 2017 - 11th International Workshop on Semantic Evaluations, SemEval 2017, Proceedings of the Workshop |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 732-736 |
Number of pages | 5 |
ISBN (Electronic) | 9781945626555 |
Publication status | Published - 2017 |
Event | 11th 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 3 → 2017 Aug 4 |
Publication series
Name | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
---|---|
ISSN (Print) | 0736-587X |
Conference
Conference | 11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 |
---|---|
Country/Territory | Canada |
City | Vancouver |
Period | 17/8/3 → 17/8/4 |
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