TADC: A Topic-Aware Dynamic Convolutional Neural Network for Aspect Extraction

• Published in: IEEE transaction on neural networks and learning systems 2023-08, Vol.34 (8), p.3912-3924
• Main Authors: Zhang, Zusheng, Rao, Yanghui, Lai, Hanjiang, Wang, Jiahai, Yin, Jian
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Aspect extraction; convolutional neural network (CNN); Convolutional neural networks; Data mining; Documents; dynamic filter; Feature extraction; Filters; Hidden Markov models; Neural networks; Recurrent neural networks; Semantics; Sentiment analysis; Support vector machines; Task analysis; topic mining; Unsupervised learning
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2021.3119026

Aspect extraction is one of the key tasks in fine-grained sentiment analysis. This task aims to identify explicit opinion targets from user-generated documents. Currently, the mainstream methods for aspect extraction are built on recurrent neural networks (RNNs), which are difficult to parallelize. To accelerate the training/testing process, convolutional neural network (CNN)-based methods are introduced. However, such models usually utilize the same set of filters to convolve all input documents, and hence, the unique information inherent in each document may not be fully captured. To alleviate this issue, we propose a CNN-based model that employs a set of dynamic filters. Specifically, the proposed model extracts the aspects in a document using the filters generated from the aspect information intrinsic in the document. With the dynamically generated filters, our model is capable of learning more important features concerning aspects, thus promoting the effectiveness of aspect extraction. Furthermore, considering that aspects can be grouped into certain topics that conversely indicate the target words that need to be extracted, we naturally introduce a neural topic model (NTM) and integrate latent topics into the CNN-based module to help identify aspects. Experiments on two benchmark datasets demonstrate that the joint model is able to effectively identify aspects and produce interpretable topics.