Abstract
Learning good semantic vector representations for sentiment analysis in phrases, sentences and paragraphs is a challenging and ongoing area of natural language processing. In this paper, we propose a Convolution Neural Network for aspect level sentiment classification. Our model first builds a convolution neural network model to aspect extraction. Afterwards, we used a sequence labeling approach with Conditional Random Fields for the opinion target detection. Finally, we concatenate an aspect vector with every word embedding and apply a convolution neural network over it to determine the sentiment towards an aspect. Results of an experiment show that our method performs comparably well on Yelp reviews.
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Xu, L., Liu, J., Wang, L., Yin, C. (2018). Aspect Based Sentiment Analysis for Online Reviews. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_78
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DOI: https://doi.org/10.1007/978-981-10-7605-3_78
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