Tzu-Hsuan Tseng

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2018

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Sentiment Analysis on Social Network: Using Emoticon Characteristics for Twitter Polarity Classification
Chia-Ping Chen | Tzu-Hsuan Tseng | Tzu-Hsuan Yang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 23, Number 1, June 2018

2017

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deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter
Tzu-Hsuan Yang | Tzu-Hsuan Tseng | Chia-Ping Chen
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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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