Abstract
With the rapid growth of social networks and e-commerce, aspect-based sentiment analysis has gradually become a vital tool to analyze and evaluate the customers’ feedback through social networking platforms or online sales websites. We could capture the customer insights as well as political opinions and predict the future social trend by analyzing the customer feedback in terms of aspects. In this research, we propose a hybrid deep learning method to solve the aspect detection problem. The aspect detection is the task to identify of the entity E and attribute A pairs expressed in the text. This model combines the advantages of each of the convolutional neural network (CNN) and long short-term memory (LSTM) methods, in which CNN works well in extracting spatial features, while LSTM works effectively in data classification. Experimental results on the Vietnamese VLSP 2018 dataset show that the proposed method achieves better results than the previous research methods if it only relies on a single method.
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Hung, B.T., Semwal, V.B., Gaud, N., Bijalwan, V. (2021). Hybrid Deep Learning Approach for Aspect Detection on Reviews. In: Singh Mer, K.K., Semwal, V.B., Bijalwan, V., Crespo, R.G. (eds) Proceedings of Integrated Intelligence Enable Networks and Computing. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-6307-6_100
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DOI: https://doi.org/10.1007/978-981-33-6307-6_100
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