Gender Opposition Recognition Method Fusing Emojis and Multi-features in Chinese Speech

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.