Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition

Jumayel Islam, Robert E. Mercer, Lu Xiao


Abstract
The advent of micro-blogging sites has paved the way for researchers to collect and analyze huge volumes of data in recent years. Twitter, being one of the leading social networking sites worldwide, provides a great opportunity to its users for expressing their states of mind via short messages which are called tweets. The urgency of identifying emotions and sentiments conveyed through tweets has led to several research works. It provides a great way to understand human psychology and impose a challenge to researchers to analyze their content easily. In this paper, we propose a novel use of a multi-channel convolutional neural architecture which can effectively use different emotion and sentiment indicators such as hashtags, emoticons and emojis that are present in the tweets and improve the performance of emotion and sentiment identification. We also investigate the incorporation of different lexical features in the neural network model and its effect on the emotion and sentiment identification task. We analyze our model on some standard datasets and compare its effectiveness with existing techniques.
Anthology ID:
N19-1137
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1355–1365
Language:
URL:
https://aclanthology.org/N19-1137
DOI:
10.18653/v1/N19-1137
Bibkey:
Cite (ACL):
Jumayel Islam, Robert E. Mercer, and Lu Xiao. 2019. Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1355–1365, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition (Islam et al., NAACL 2019)
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PDF:
https://aclanthology.org/N19-1137.pdf