Abstract
Aspect-Based (also known as aspect-level) Sentiment Classification (ABSC) aims at determining the sentimental tendency of a particular target in a sentence. With the successful application of the attention network in multiple fields, attention-based ABSC has aroused great interest. However, most of the previous methods are difficult to parallelize, insufficiently obtain, and fuse the interactive information. In this paper, we proposed a Multiple Interactive Attention Network (MIN). First, we used the Bidirectional Encoder Representations from Transformers (BERT) model to pre-process the data. Then, we used the partial transformer to obtain a hidden state in parallel. Finally, we took the target word and the context word as the core to obtain and fuse the interactive information. Experimental results on the different datasets showed that our model was much more effective.
Highlights
With the development of social networks, more and more users are willing to share their opinions on the Internet, comment information is rapidly expanding, and it is difficult to process large amounts of information with sentiment analysis alone
This paper proposed a model named Multiple Interactive Attention Network (MIN) to address these problems
Gated Recurrent Neural Networks (GRU) is a variant of RNN, and it addresses the problem of gradient disappearance to a certain extent by delicate gate control, like Long Short-Term Memory (LSTM)
Summary
With the development of social networks, more and more users are willing to share their opinions on the Internet, comment information is rapidly expanding, and it is difficult to process large amounts of information with sentiment analysis alone. Neural Network (RNN) [5], is designed to automatically learn useful low dimensional representations from targets and contexts They are difficult to implement a parallel operation, and there is a gradient disappearance problem. The attention mechanism with RNN has been successfully used for machine translation [6], and these methods have been extensively used in other fields Using these methods, we can make the sentiment analysis model, selectively balancing the weight of context words and target. To address the first problem, we took advantage of Multi-Head Attention (MHA) to obtain useful interactive information. To address another problem, we adopted target-context pair and Context-Target-Interaction (CTI) in our model.
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