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%.- Anthology ID:
- S17-2123
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 732–736
- Language:
- URL:
- https://aclanthology.org/S17-2123
- DOI:
- 10.18653/v1/S17-2123
- Cite (ACL):
- Joosung Yoon, Kigon Lyu, and Hyeoncheol Kim. 2017. Adullam at SemEval-2017 Task 4: Sentiment Analyzer Using Lexicon Integrated Convolutional Neural Networks with Attention. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 732–736, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Adullam at SemEval-2017 Task 4: Sentiment Analyzer Using Lexicon Integrated Convolutional Neural Networks with Attention (Yoon et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/S17-2123.pdf