Semi-supervised domain adaptation and collaborative deep learning for dual sentiment analysis
| Autor: | S H Manjula, Savitha Mathapati, Ayesha Nafeesa, K. R. Venugopal, R Tanuja |
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| Rok vydání: | 2019 |
| Předmět: |
Domain adaptation
Computer science business.industry General Chemical Engineering Deep learning Training time Sentiment analysis General Engineering General Physics and Astronomy Machine learning computer.software_genre Convolutional neural network Recurrent neural network General Earth and Planetary Sciences Labeled data General Materials Science Artificial intelligence business Classifier (UML) computer General Environmental Science |
| Zdroj: | SN Applied Sciences. 1 |
| ISSN: | 2523-3971 2523-3963 |
| DOI: | 10.1007/s42452-019-0943-0 |
| Popis: | Sentiment classification is a much needed topic that has grabbed the interest of many researchers. Especially, classification of data from customer reviews on various commercial products has been an important source of research. A model called supervised dual sentiment analysis is used to handle the polarity shift problem that occurs in sentiment classification. Labeling the reviews is a tedious and time consuming process. Even, a classifier trained on one domain may not perform well on the other domain. To overcome these limitations, in this paper we propose semi-supervised domain adaptive dual sentiment analysis that train a domain independent classifier with few labeled data. Reviews are of varying length and hence, classification is more accurate if long term dependency between the words is considered. We propose a collaborative deep learning approach to the dual sentiment analysis. Long short term memory (LSTM) recurrent neural network is used to handle sequence prediction to classify the reviews more accurately. LSTM takes more time to extract features from the reviews. Convolution neural network is used before LSTM layers to extract features resulting in the reduction of training time compared to LSTM alone. |
| Databáze: | OpenAIRE |
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