Abstract. Sentiment analysis is an important task in natural language processing and has a wide range of applications. This paper describes our deep learning approach to multilingual aspect-based sentiment analysis. Our model use a deep convolutional neural network for both aspect extraction and aspect-based sentiment analysis. We take aspect extraction as a multi-label classification problem, outputting probabilities over aspects parameterized by a threshold. For the sentiment towards an aspect, we concatenate an aspect vector with every word embedding and apply a convolution over it. Experiments result shows that our system performs comparably well on the Yelp reviews.

Abstract. This paper proposes a data extraction method based on visual recognition and Document Object Model(DOM) tree for Deep Pages to extract a large number of Deep Web data in-formation. By utilizing the characteristics of the presentation of Deep Web data and the characteristics of the visual information of the web page, the data region of multiple targets is located, and the data of the data region is extracted accurately by DOM analysis. Experiments were conducted on several travel websites, and test results show that efficiency and accuracy of the extraction are higher than those of the traditional methods.

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