Abstract
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis technology. In recent years, neural networks are widely used to extract features of aspects and contexts and proven to have a dramatic improvement in retrieving the sentiment feature from comments. However, due to the increasing complexity of comment information, only considering sentence or word features, respectively, may cause the loss of key text information. Besides, characters have more microscopic features, so the fusion of features between three different levels, such as sentences, words, and characters, should be taken into consideration for exploring their internal relationship among different granularities. According to the above analysis, we propose a multifeature interactive fusion model for aspect-based sentiment analysis. Firstly, the text is divided into two parts: contexts and aspects; then word embedding and character embedding are associated to further explore the potential features. Secondly, to establish a close relationship between contexts and aspects, features fusion of both aspects and contexts are exploited in our model. Moreover, we apply the attention mechanism to calculate fusion weight of features, so that the key features information plays a more significant role in the sentiment analysis. Finally, we experimented on the three datasets of SemEval2014. The results of experiment showed that our model has a better performance compared with the baseline models.
Highlights
Sentiment analysis is an important and fundamental task in natural language processing, which aims to analyse the sentiment polarity of text
Among the baselines of neural network, the performance of the LSTM is worst. e performance of TD-LSTM is better than LSTM. e reason is that the TD-LSTM method inputs the contexts and aspects separately
AE-LSTM performs better than the LSTM model, because AE-LSTM separates the aspects and contexts in the model, and combines the attention mechanism to obtain the weight of the feature representations
Summary
Sentiment analysis is an important and fundamental task in natural language processing, which aims to analyse the sentiment polarity of text. With the development of deep learning, more intelligent learning methods have been proposed [3,4,5,6], and researchers have applied neural network to construct feature representation system for sentiment analysis [7,8,9]. Tang et al [11] divided text into aspects and its contexts and fed them to the aspectbased sentiment analysis based on the long short-term memory network model. Ma et al [12] made a combination of long short-term memory network and attention mechanism to construct the IAN model between aspects and its contexts These methods are much better than the traditional methods, the performance of ABSA is not good enough, because the extracted features are insufficient.
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