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Aspect-level implicit sentiment analysis model based on semantic wave and knowledge enhancement

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Abstract

Most implicit sentiment sentences in aspect level implicit sentiment analysis lack emotional words, but there are still potential emotional clues. Current research mainly utilizes contextual information and external knowledge of text for semantic enhancement, thereby improving implicit sentiment analysis. However, in these models, the semantic correlation between words decays as the distance between the two increases, leading to weak and difficult to capture emotional cues at long distances, presenting ambiguity in emotional polarity and reducing the performance of sentiment analysis. To solve this problem, in this paper, we propose aspect-level implicit sentiment analysis model based on semantic wave and knowledge enhancement. Firstly, the semantic wave-based aspect word context information enhancement method uses the fluctuation property of semantic wave to perform weight adaptive enhancement of semantically related long-distance context information. This method essentially utilizes a sine wave function to significantly reduce the weight of words that are close but semantically unrelated, thereby allowing the weight of words that are close and semantically similar to be relatively reflected, in order to solve the problem of hiding emotional clues at long distances. Then, the implicit sentiment analysis method based on supervised contrastive pre-training is used to capture potential sentiment cues from context information and embed them into the sentence vector by weak feature learning of implicit sentiment. Finally, multi-feature interaction-based knowledge enhancement method is used to focus on the context content of sentences that are more related to background knowledge in a feature interaction way, and the information scaling factor is combined to enhance the dependency between aspect words and sentiment cues, thereby solving the problem of weak features in long-distance emotional clues and accurately identifying the emotional polarity of aspect words. The experiment shows that the model proposed in this paper has made significant improvements on the Restaurant and Laptop datasets in SemEval 2014 Task 4, with an accuracy increase of 3.38% and 4.57% in implicit emotions, respectively, achieving the best performance so far. At the same time, generalization and robustness testing experiments were conducted on the model, achieving preferable performance improvement.

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Data availability

All datasets are commonly used in NLP. The corresponding links are as follows: https://semeval.github.io/SemEval2021/tasks.

Code availability

If the paper is accepted, we are pleased to share the code.

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Funding

This work was supported by Key Research Planning Project of National Language Committee (No. ZDI145-94).

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  1. Maoyuan Zhang and Fei Wu have contributed equally to this work.

Authors and Affiliations

  1. Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei, 430079, People’s Republic of China

    Maoyuan Zhang, Fei Wu, WeiLiang Chen & Xiang Li

  2. School of Computer Science, Central China Normal University, Wuhan, Hubei, 430079, People’s Republic of China

    Maoyuan Zhang, Fei Wu, WeiLiang Chen & Xiang Li

  3. National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei, 430079, People’s Republic of China

    Maoyuan Zhang

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Contributions

All authors contributed to the study conception and design. Code implementation, data collection and analysis were performed by Maoyuan Zhang, Fei Wu, WeiLiang Chen and Xiang Li. The first draft of the manuscript was written by Fei Wu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. All authors contributed equally to this paper.

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Correspondence to WeiLiang Chen.

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Zhang, M., Wu, F., Chen, W. et al. Aspect-level implicit sentiment analysis model based on semantic wave and knowledge enhancement. J Supercomput 80, 22726–22747 (2024). https://doi.org/10.1007/s11227-024-06255-x

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