Hyeoncheol Kim


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Multi-Channel Lexicon Integrated CNN-BiLSTM Models for Sentiment Analysis
Joosung Yoon | Hyeoncheol Kim
Proceedings of the 29th Conference on Computational Linguistics and Speech Processing (ROCLING 2017)

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Adullam at SemEval-2017 Task 4: Sentiment Analyzer Using Lexicon Integrated Convolutional Neural Networks with Attention
Joosung Yoon | Kigon Lyu | Hyeoncheol Kim
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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%.