Variants of Long Short-Term Memory for Sentiment Analysis on Vietnamese Students’ Feedback Corpus
| Autor: | Vu Duc Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Dependency (UML)
business.industry Computer science Vietnamese Sentiment analysis 02 engineering and technology English language 010501 environmental sciences computer.software_genre 01 natural sciences language.human_language Support vector machine Long short term memory Support vector machine classifier 0202 electrical engineering electronic engineering information engineering language 020201 artificial intelligence & image processing Artificial intelligence business Hidden Markov model computer Natural language processing 0105 earth and related environmental sciences |
| Zdroj: | KSE |
| DOI: | 10.1109/kse.2018.8573351 |
| Popis: | The Long Short-Term Memory (LSTM) and Dependency Tree-LSTM have shown the state-of-the-art results for the sentiment analysis task for the English language. Despite many studies of LSTM approach, there are no studies of Dependency Tree-LSTM approach for Vietnamese sentiment analysis. In this paper, we conducted experiments with LSTM, Dependency Tree-LSTM, and our proposed models on Vietnamese Students’ Feedback Corpus. According to the experimental results, the Dependency Tree-LSTM were not better than the LSTM model. However, when combining final hidden state vectors of LSTM and Dependency Tree-LSTM models with a Support Vector Machine classifier, we achieved the F1-score of 90.2%, which is higher than the performance of the LSTM model. |
| Databáze: | OpenAIRE |
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