@inproceedings{5b9c344cfd314154a7061d55726d6f65,
title = "Multi-channel lexicon integrated CNN-BILSTM models for sentiment analysis",
abstract = "We improved sentiment classifier for predicting document-level sentiments from Twitter by using multi-channel lexicon embedidngs. The core of the architecture is based on CNN-BiLSTM that can capture high level features and long term dependency in documents. We also applied multi-channel method on lexicon to improve lexicon features. The macro-averaged F1 score of our model outperformed other classifiers in this paper by 1-4%. Our model achieved F1 score of 64% in SemEval Task 4 (2013-2016) datasets when multichannel lexicon embedding was applied with 100 dimensions of word embedding.",
keywords = "CNN-BiLSTM, Deep Learning, Lexicon, Multi-Channel, Sentiment analysis",
author = "Joosung Yoon and Hyeoncheol Kim",
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: {\textcopyright} The Association for Computational Linguistics and Chinese Language Processing; 29th Conference on Computational Linguistics and Speech Processing, ROCLING 2017 ; Conference date: 27-11-2017 Through 28-11-2017",
year = "2017",
month = nov,
day = "1",
language = "English",
series = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing, ROCLING 2017",
publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
pages = "244--253",
editor = "Lun-Wei Ku and Yu Tsao and Chi-Chun Lee and Cheng-Zen Yang and Hung-Yi Lee and Tsai, {Richard T.-H.} and Wen-Hsiang Lu and Shih-Hung Wu",
booktitle = "Proceedings of the 29th Conference on Computational Linguistics and Speech Processing, ROCLING 2017",
}