[Objective] This paper proposes a deep learning model——AttentionSBGMC to improve the automatic classification of citation sentiment and purposes. [Methods] First, we used the SciBERT pre-training model to obtain the semantic representation vector for the sentences. Then, according to the characteristics of the texts, we used the BiGRU neural network and the multi-scale convolutional neural network (Multi-CNN) to extract their temporal global features and local key features. Third, we utilized the attention model to highlight the key features by redistributing the extracted features’ weights. Finally, we finished the classification tasks with the help of linear layers. [Results] We examined the new method with two citation data sets. With Abu-Jbara data set the F1 values in three classification tasks (for subjective and objective citation emotion, positive and negative citation emotion, and citation purpose) were 86.74%, 91.14% and 84.92%, respectively. With Athar data set the F1 values in two classification tasks (for subjective and objective citation emotion, positive and negative citation emotion) were 88.50%, 86.59%, respectively. [Limitations] The proposed model was only examined on English data sets, which needs to be expanded in the future. [Conclusions] The proposed model could effectively extract the important corpus features, and automatically classify citation sentiment and purposes.
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