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

Joosung Yoon, Kigon Lyu, Hyeoncheol Kim


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://www.aclweb.org/anthology/S17-2123
DOI:
10.18653/v1/S17-2123
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2123.pdf