Sentiment analysis using multi neighborhood representation learning embeddings: graph convolutional neural networks

Da Lv; Xiaoxia Liu

doi:10.1117/12.3004108

11 October 2023 Sentiment analysis using multi neighborhood representation learning embeddings: graph convolutional neural networks

Da Lv, Xiaoxia Liu

Author Affiliations +

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128005D (2023) https://doi.org/10.1117/12.3004108
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

With the thriving of social networks, sentiment analysis at aspect-level has become a research hotspot in the field of natural language processing, aiming to extract user expressed sentiment from massive text data. To tackle the problem that current sentiment analysis methods cannot simultaneously consider the contextual and syntactic information of aspect terms, this paper proposes a targeted model for aspect-level sentiment analysis by embedding different neighborhood representations. By integrating aspect neighborhood information and node neighborhood information through convolutional neural networks, our proposed model achieves high effectiveness on five public datasets as demonstrated by the evaluation coefficients constructed in our study.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Da Lv and Xiaoxia Liu "Sentiment analysis using multi neighborhood representation learning embeddings: graph convolutional neural networks", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128005D (11 October 2023); https://doi.org/10.1117/12.3004108

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