Abstract
Aspect-based analysis currently becomes a hot topic in opinion mining and sentiment analysis. The major task here is how to detect rating and weighting for each aspect based on an input of a collection of users’ reviews in which only the overall ratings are given. Previous studies usually use a bag-of-word model for representing aspects thus may fail to capture semantic relations between words and cause an inaccuracy of aspect ratings prediction. To overcome this drawback, in this paper we will propose a model for aspect analysis, in which we first use a new deep learning technique from [8] for representing paragraphs and then integrate these representations into a neural network model to infer aspect ratings and aspect weights. The experiments are carried out on the data collected from hotel services with the aspects including “cleanliness”, “location”, “service”, “room”, and “value”. Experimental results show that our proposed method outperforms the well known studies for the same problem.
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This paper is partly funded by The Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.01-2014.22.
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Pham, DH., Le, AC., Nguyen, TTT. (2016). Determing Aspect Ratings and Aspect Weights from Textual Reviews by Using Neural Network with Paragraph Vector Model. 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_27
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DOI: https://doi.org/10.1007/978-3-319-42345-6_27
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