Sentiment analysis, including aspect-level sentiment classification, is an important basic natural language processing (NLP) task. Aspect-level sentiment can provide complete and in-depth results. Words with different contexts variably influence the aspect-level sentiment polarity of sentences, and polarity varies based on different aspects of a sentence. Recurrent neural networks (RNNs) are regarded as effective models for handling NLP and have performed well in aspect-level sentiment classification. Extensive literature exists on sentiment classification that utilizes convolutional neural networks (CNNs); however, no literature on aspect-level sentiment classification that uses CNNs is available. In the present study, we develop a CNN model for handling aspect-level sentiment classification. In our model, attention-based input layers are incorporated into CNN to introduce aspect information. In our experiment, in which a benchmark dataset from Twitter is compared with other models, incorporating aspect information into CNN improves aspect-level sentiment classification performance without using syntactic parser or other language features.

Sentiment analysis is a basic task of natural language processing, while aspect level sentiment analysis is an important topic in sentiment analysis. In the same sentence, different words have different influence on the sentiment polarity of aspect, so the key to solve the problem is how to build a relation model between the aspect and the words in the sentence. In this paper, by using two recurrent networks, we built a model for sentence and introduced attention mechanism to fuse aspect information, so as to achieve a better effect. An experiment on public dataset show that the proposed algorithm obtain a better result without carrying out complex feature engineering.

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