@inproceedings{yang-etal-2017-deepsa,
title = "deep{SA} at {S}em{E}val-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in {T}witter",
author = "Yang, Tzu-Hsuan and
Tseng, Tzu-Hsuan and
Chen, Chia-Ping",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2101",
doi = "10.18653/v1/S17-2101",
pages = "616--620",
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.",
}
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%0 Conference Proceedings
%T deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter
%A Yang, Tzu-Hsuan
%A Tseng, Tzu-Hsuan
%A Chen, Chia-Ping
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F yang-etal-2017-deepsa
%X 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.
%R 10.18653/v1/S17-2101
%U https://aclanthology.org/S17-2101
%U https://doi.org/10.18653/v1/S17-2101
%P 616-620
Markdown (Informal)
[deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter](https://aclanthology.org/S17-2101) (Yang et al., SemEval 2017)
ACL