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Abstract
Speech with gender opposition on the internet have been causing antagonism, gamophobia, and pregnancy phobia among young groups. Recognizing gender opposition speech contributes to maintaining a healthy online environment and security in cyberspace. Traditional recognition model ignores the Chinese-owned features and emojis, which inevitably affects the recognition accuracy of gender opposition. To tackle this issue, a gender opposition recognition method fusing emojis and multi-features in Chinese speech(GOR-CS) is proposed. Firstly, the exBERT method is employed to expand the encoding of emojis into the BERT vocabulary, which can ensure BERT to extract the basis vectors containing characters and emojis information. Then, the feature vectors containing Wubi, Zhengma, and Pinyin information are extracted by Word2Vec to obtain the Chinese-owned features of gender opposition text. Further, the proposed basis vector and feature vectors are fused and then fed into the Bi-GRU network to extract deeper semantics from input sentences. Finally, to determine whether the speech are related to gender opposition, the sentiment polarities are calculated with the fully connected layer and SoftMax function. Experimental results show that the proposed method can effectively improve the accuracy of gender opposition recognition.
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The datasets generated during and/or analysed during the current study are not publicly available due to Protect data security but are available from the corresponding author on reasonable request.
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Funding
This work was supported by the National Natural Science Foundation of China (Grant no. 62076006), the Opening Foundation of the State Key Laboratory of Cognitive Intelligence, iFLYTEK (Grant no. COGOS-2023HE02), and the University Synergy Innovation Program of Anhui Province (Grant no GXXT-2021-008).
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School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, Anhui, China
Shunxiang Zhang, Zichen Ma, Hanchen Li & Yunduo Liu
School of Computer, Huainan Normal University, Huainan, Anhui, China
Shunxiang Zhang & Lei Chen
Department of Computer Science and Information Engineering (CSIE), Providence University, Taichung, Taiwan, Republic of China
Kuan-Ching Li
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Shunxiang Zhang, Zichen Ma and Hanchen Li. The first draft of the manuscript was written by Shunxiang Zhang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Correspondence toShunxiang Zhang.
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Zhang, S., Ma, Z., Li, H.et al. Gender opposition recognition method fusing emojis and multi-features in Chinese speech.Soft Comput29, 2379–2390 (2025). https://doi.org/10.1007/s00500-025-10492-4
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