HGRU-Mamba:State Space Model for Implicit Sentiment Classification in Chinese

2025 · doi:10.22541/au.174531626.62481799/v1
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Abstract

Due to the rich lexical meanings and unique characteristics of the Chinese language, Chinese comments often lack explicit sentiment words, making implicit sentiment analysis challenging. This paper focuses on Chinese implicit sentiment analysis using the state-space model. We introduce a hierarchical feature network and forgetting gate residual structure to extract sentiment features at both the word and sentence levels, enabling sentiment classification. The main contributions are: (1) Adopting a state-space model architecture: We utilize the selective state-space model (Mamba) for efficient feature extraction, reducing training costs. (2) Introducing a hierarchical feature network: The HGRU network extracts word- and sentence-level features to identify sentiment. (3) Introducing an oblivious gate residual network: This improves model robustness. Experiments on three Chinese sentiment datasets (Weibo, chnsenticorp-htl, and, SMP-ECISA2019) show significant accuracy improvement.
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Abstract

Due to the rich lexical meanings and unique characteristics of the Chinese language, Chinese comments often lack explicit sentiment words, making implicit sentiment analysis challenging. This paper focuses on Chinese implicit sentiment analysis using the state-space model. We introduce a hierarchical feature network and forgetting gate residual structure to extract sentiment features at both the word and sentence levels, enabling sentiment classification. The main contributions are: (1) Adopting a state-space model architecture: We utilize the selective state-space model (Mamba) for efficient feature extraction, reducing training costs. (2) Introducing a hierarchical feature network: The HGRU network extracts word- and sentence-level features to identify sentiment. (3) Introducing an oblivious gate residual network: This improves model robustness. Experiments on three Chinese sentiment datasets (Weibo, chnsenticorp-htl, and, SMP-ECISA2019) show significant accuracy improvement. Supplementary Material File (hgru-mambastate space model for implicit sentiment.pdf) - Download - 519.91 KB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

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Authors Metrics & Citations Metrics Article Usage 309views 154downloads Citations Download citation Haoran LIU, zipeng yang, Ke ZHANG, et al. HGRU-Mamba:State Space Model for Implicit Sentiment Classification in Chinese. Authorea. 22 April 2025. DOI: https://doi.org/10.22541/au.174531626.62481799/v1 DOI: https://doi.org/10.22541/au.174531626.62481799/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.

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last seen: 2026-05-19T01:45:01.086888+00:00