Abstract
Sentiment analysis has become an important topic in the field of natural language processing. Short text possesses multiple characteristics such as semantic sparsity and polysemy, which bring numerous challenges to short text sentiment analysis. Currently, the sentiment analysis of short text is insufficient and faces many problems such as feature semantics loss that may occur in the training process and the features that are used to classify with excessive dimension. Aiming at the existing problems, we proposed a short text sentiment classification model with a sentence vector enhancement mechanism (ERNIE-BiGRU-SVE-DPCNN, EBSD). This model uses Bi-GRU to extract the word order features of short text and uses DPCNN to reduce the dimension of features that are used for classification. Based on the group-wise enhancement mechanism, we proposed a Sentence Vector Enhancement Mechanism (SVE), which can enhance the features according to the sentence vector generated by ERNIE. The purpose of the SVE is to reduce the loss of semantic meanings. The result of experiments on the short text dataset online_shopping_10_cats has shown that the EBSD has higher accuracy than other baselines.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Center, C.I.N.I.: The 51th China Statistical Report on Internet Development. Tech. rep., CNNIC, Beijing (2023)
Jia, W., Peng, J.: The public sentiment analysis of double reduction policy on Weibo platform. Computat. Intell. Neurosci. 2022 (2022)
Zhou, Z.G.: Research on sentiment analysis model of short text based on deep learning. Sci. Program. 2022 (2022)
Chen, Q., Sun, X., Wang, J., Wang, M.: User-based hierarchical network of Sina Weibo emotion analysis. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 22(5), 1–22 (2023)
Yu, L., Duan, Y., Li, K.C.: A real-world service mashup platform based on data integration, information synthesis, and knowledge fusion. Connect. Sci. 33(3), 463–481 (2021)
Dai, Y., Wang, T.: Prediction of customer engagement behaviour response to marketing posts based on machine learning. Connect. Sci. 33(4), 891–910 (2021)
Xu, G., Yao, H., Wu, D., Li, Y., Ouyang, D., Chen, G.: Public opinion classification and text alignment based on Chinese and Tibetan corpus. Clust. Comput. 22, 10263–10274 (2019)
Yang, Z., Wu, Q., Venkatachalam, K., Li, Y., Xu, B., Trojovskỳ, P.: Topic identification and sentiment trends in Weibo and Wechat content related to intellectual property in China. Technol. Forecast. Soc. Chang. 184, 121980 (2022)
Zhou, Y., Li, J., Chi, J., Tang, W., Zheng, Y.: Set-CNN: a text convolutional neural network based on semantic extension for short text classification. Knowl.-Based Syst. 257, 109948 (2022)
Cui, H., Wang, G., Li, Y., Welsch, R.E.: Self-training method based on GCN for semi-supervised short text classification. Inf. Sci. 611, 18–29 (2022)
Wei, Z., Liu, W., Zhu, G., Zhang, S., Hsieh, M.Y.: Sentiment classification of Chinese Weibo based on extended sentiment dictionary and organisational structure of comments. Connect. Sci. 34(1), 409–428 (2022)
Onan, A.: Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification. J. King Saud Univ. Comput. Inf. Sci. 34(5), 2098–2117 (2022)
Naseem, U., Razzak, I., Musial, K., Imran, M.: Transformer based deep intelligent contextual embedding for twitter sentiment analysis. Futur. Gener. Comput. Syst. 113, 58–69 (2020)
Pavan Kumar, M., Jayagopal, P.: Context-sensitive lexicon for imbalanced text sentiment classification using bidirectional LSTM. J. Intell. Manuf. 34(5), 2123–2132 (2023)
Zhang, B., Zhou, W.: Transformer-encoder-GRU (T-E-GRU) for Chinese sentiment analysis on Chinese comment text. Neural Process. Lett. 55(2), 1847–1867 (2023)
Yan, C., Liu, J., Liu, W., Liu, X.: Research on public opinion sentiment classification based on attention parallel dual-channel deep learning hybrid model. Eng. Appl. Artif. Intell. 116, 105448 (2022)
Sun, X., Huo, X.: Word-level and pinyin-level based Chinese short text classification. IEEE Access 10, 125552–125563 (2022)
Yang, T., Hu, L., Shi, C., Ji, H., Li, X., Nie, L.: HGAT: heterogeneous graph attention networks for semi-supervised short text classification. ACM Trans. Inf. Syst. (TOIS) 39(3), 1–29 (2021)
Jin, Z., Tao, M., Zhao, X., Hu, Y.: Social media sentiment analysis based on dependency graph and co-occurrence graph. Cogn. Comput. 14(3), 1039–1054 (2022)
Li, Y., Li, N.: Sentiment analysis of Weibo comments based on graph neural network. IEEE Access 10, 23497–23510 (2022)
Liu, W., Chen, X., Liu, J., Feng, S., Sun, Y., Tian, H., et al.: Ernie 3.0 tiny: frustratingly simple method to improve task-agnostic distillation generalization (2023). arXiv:2301.03416
Dharma, E.M., Gaol, F.L., Warnars, H., Soewito, B.: The accuracy comparison among word2vec, glove, and fasttext towards convolution neural network (CNN) text classification. J. Theor. Appl. Inf. Technol. 100(2), 31 (2022)
Johnson, R., Zhang, T.: Deep pyramid convolutional neural networks for text categorization. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 562–570. Association for Computational Linguistics, Vancouver, Canada (2017). https://doi.org/10.18653/v1/P17-1052. https://aclanthology.org/P17-1052
Xu, D., Tian, Z., Lai, R., Kong, X., Tan, Z., Shi, W.: Deep learning based emotion analysis of microblog texts. Inf. Fusion 64, 1–11 (2020)
Jiang, W., Zhou, K., Xiong, C., Du, G., Ou, C., Zhang, J.: KSCB: a novel unsupervised method for text sentiment analysis. Appl. Intell. 53(1), 301–311 (2023)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Alsaleh, D., Larabi-Marie-Sainte, S.: Arabic text classification using convolutional neural network and genetic algorithms. IEEE Access 9, 91670–91685 (2021)
Yao, L., Mao, C., Luo, Y.: Graph convolutional networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7370–7377 (2019)
Liu, R., Wang, H., Li, Y.: AgriMFLN: Mixing features LSTM networks for sentiment analysis of agricultural product reviews. Appl. Sci. 13(10), 6262 (2023)
Cheng, W., Wang, Y., Li, L., Wang, S.: Classification of e-commerce reviews sentiment tendency based on ALBERT-SVM. In: EEI 2022; 4th International Conference on Electronic Engineering and Informatics, pp. 1–4 (2022)
Acknowledgement
Supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region No. (2022001C427, 2022001C429).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, Z., Zhang, L., Zhao, K., Maimaiti, M., Bi, X., Fan, H. (2025). EBSD: Short Text Sentiment Classification Using Sentence Vector Enhancement Mechanism. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15033. Springer, Singapore. https://doi.org/10.1007/978-981-97-8502-5_24
Download citation
DOI: https://doi.org/10.1007/978-981-97-8502-5_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-8501-8
Online ISBN: 978-981-97-8502-5
eBook Packages: Computer ScienceComputer Science (R0)