Shao-Cheng Huang


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SCUDS at ROCLING-2021 Shared Task: Using Pretrained Model for Dimensional Sentiment Analysis Based on Sample Expansion Method
Hsiao-Shih Chen | Pin-Chiung Chen | Shao-Cheng Huang | Yu-Cheng Chiu | Jheng-Long Wu
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

Sentiment analysis has become a popular research issue in recent years, especially on educational texts which is an important problem. According to literature, the similar sentence generation can help the prediction performance of machine learning. Therefore, the process of controlled expansional samples is a key component to prediction models. The paper proposed a sample expansion method which combined part-of-speech filter and similar word finder of Word2Vec. The generate samples have high quality with similar sentiment representation. The DistilBERT pretrained model is used to learn and predict Valence-Arousal scores from the expansion samples. Experimental result displays that the using the expansion samples as training data into prediction model has outperforms original training data without expansion, and obtains 80% mean square error reducing and 28% pearson correlation coefficient increasing.