deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter

Tzu-Hsuan Yang, Tzu-Hsuan Tseng, Chia-Ping Chen

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Abstract
In this paper, we describe our system implementation for sentiment analysis in Twitter. This system combines two models based on deep neural networks, namely a convolutional neural network (CNN) and a long short-term memory (LSTM) recurrent neural network, through interpolation. Distributed representation of words as vectors are input to the system, and the output is a sentiment class. The neural network models are trained exclusively with the data sets provided by the organizers of SemEval-2017 Task 4 Subtask A. Overall, this system has achieved 0.618 for the average recall rate, 0.587 for the average F1 score, and 0.618 for accuracy.
Anthology ID:
S17-2101
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Editors:
Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
616–620
Language:
URL:
https://aclanthology.org/S17-2101
DOI:
10.18653/v1/S17-2101
Bibkey:
Cite (ACL):
Tzu-Hsuan Yang, Tzu-Hsuan Tseng, and Chia-Ping Chen. 2017. deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 616–620, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter (Yang et al., SemEval 2017)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/S17-2101.pdf
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