Abstract
Recent years, many studies have addressed problems in sentiment analysis at different levels, and building aspect-based methods has become a central issue for deep opinion mining. However, previous studies need to use two separated modules in order to extract aspect-sentiment word pairs, then predict the sentiment polarity. In this paper, we use Restricted Boltzmann Machines in combination with Word Embedding model to build the joined model which not only extracts aspect terms appeared and classifies them into respective categories, but also completes the sentiment polarity prediction task. The experimental results show that the method we use in aspect-based sentiment analysis tasks is better than other state-of-the-art approaches.
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This research is supported by research funding from Honors Program, University of Science, Vietnam National University - Ho Chi Minh City.
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Nguyen-Hoang, BD., Ha, QV., Nghiem, MQ. (2016). Aspect-Based Sentiment Analysis Using Word Embedding Restricted Boltzmann Machines. In: Nguyen, H., Snasel, V. (eds) Computational Social Networks. CSoNet 2016. Lecture Notes in Computer Science(), vol 9795. Springer, Cham. https://doi.org/10.1007/978-3-319-42345-6_25
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DOI: https://doi.org/10.1007/978-3-319-42345-6_25
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