Abstract
Sentiment analysis models based on neural network architecture have achieved promising results. Some works bring improvement to these neural models via taking user and product into account. However, the way of utilizing significant role user and product by now is limited to embed them into vectors on word or semantic level, and ignore statistic information carried by them such as all the marks given by one user. In this paper, we propose a novel neural classifier, which extracts and feeds statistic information carried by user and product to neural networks. Our proposed method can utilize user preference and product characteristics so as to yield excellent performance on sentiment analysis. To fully evaluate the efficiency of our model, we conduct experiment on three popular sentiment datasets: IMDB, Yelp13 and Yelp14. And the experiment results show that our model achieves state-of-the-art on all three datasets .
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Li, C., Xie, J., Xing, Y. (2019). Neural Classifier with Statistic Information of User and Product for Sentiment Analysis. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11839. Springer, Cham. https://doi.org/10.1007/978-3-030-32236-6_33
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DOI: https://doi.org/10.1007/978-3-030-32236-6_33
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