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Effective inter-aspect words modeling for aspect-based sentiment analysis

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Abstract

Aspect-based sentiment analysis (ABSA) is a prominent and challenging issue in natural language processing tasks. It aims to analyze the emotion of the aspect words in given subjective sentences. A subjective sentence usually contains one or more aspect words, and there are potential associations between different aspect words. At present, many works in the literature ignore the potential relationship between aspect words. Therefore, in this paper, we propose an oriented inter-aspect modeling hierarchical network (IA-HiNET), which aims to mine and strengthen the relationship between different aspect words, and further realize the task of sentence-level sentiment analysis based on aspect words. Specifically, we introduce part-of-speech information and position information as a priori knowledge, and then construct a graph convolution network (GCN) based on sentence dependency to capture emotional cues related to aspect words. We design an aspect-oriented self-attention mechanism to map different aspect words with the same attribute into the same vector space to determine the correlation between different aspect words. Furthermore, we design a novel information gate mechanism to filter the emotional features unrelated to aspect words. The indicative importance between different aspect words is also used to assist the aspect-based sentence-level affective analysis task. We carry out experiments on four benchmark datasets, and excellent experimental results show the effectiveness of our model.

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Acknowledgements

This work is based on the paper “Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks” and the experiment from the work because the authors provided easy-to-read and easy-to-implement experimental code to help us learn and perform experiments. Meanwhile, this work is supported partly by the National Natural Science Foundation of China (NO. 62166041) and by the Xin Jiang Education Funds Project of China (No. 90390007).

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Authors and Affiliations

  1. Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China

    Tiquan Gu, Hui Zhao & Min Li

  2. Key Laboratory of Multilingual Information Technology in Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China

    Tiquan Gu, Hui Zhao & Min Li

  3. College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, China

    Tiquan Gu, Hui Zhao & Min Li

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  1. Tiquan Gu
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Correspondence to Hui Zhao.

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Gu, T., Zhao, H. & Li, M. Effective inter-aspect words modeling for aspect-based sentiment analysis. Appl Intell 53, 4366–4379 (2023). https://doi.org/10.1007/s10489-022-03630-0

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