Abstract
Focusing on the diversified opinion expression form and the explosive growth of information amount in network environment of big data, we propose a text emotion recognition model based on multi-dimensional LSTM to improve classification accuracy of network information by making full use of additional information of text samples. In this paper, we divide the original sample into two parts: the main information sample and the additional information sample. Then multi-dimensional LSTM model is used to extract their features vectors. Finally, according to the results of the two feature vectors, the classification result is carried out by feature fusion and further computation. The multi-dimensional LSTM model is implemented and tested by TensorFlow. The experimental results show that the emotion recognition classification accuracy has been greatly improved by taking advantage of multi-dimensional LSTM in big data environment.
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
References
Hao, F.: Emotion recognition simulation of Japanese text based on FPGA and neural network. Microprocess. Microsyst. (2020)
Ho, F., Yang, S.: Multimodal approach of speech emotion recognition using multi-level multi-head fusion attention-based recurrent neural network. IEEE Access 8, 61672–61686 (2020)
Yao, F., Wang, S.: Speech emotion recognition using fusion of three multi-task learning-based classifiers: HSF-DNN, MS-CNN and LLD-RNN. Speech Commun. 120, 11–19 (2020)
Liu, F., Zheng, S.: HieNN-DWE: a hierarchical neural network with dynamic word embeddings for document level sentiment classification. Neurocomputing 403, 21–32 (2020)
Wang, F., Chen, S.: HieNN-DWE: improved Danmaku emotion analysis and its application based on Bi-LSTM model. IEEE Access 8, 114123–114134 (2020)
Wan, Y.: Short-text sentiment classification based on graph-LSTM. In: AIAM, pp. 35–38(2019)
Ding, F., Cheng, S.: The power of the “like’’ button: the impact of social media on box office. Decis. Support Syst. 94, 77–84 (2017)
Funding
This research was funded by the Shandong Provincial Natural Science Foundation under Project ZR2019MF034.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, W., Liu, X., Shi, L., Liu, Y., Song, Y. (2021). Multi-dimensional LSTM: A Model of Network Text Classification. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12939. Springer, Cham. https://doi.org/10.1007/978-3-030-86137-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-86137-7_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86136-0
Online ISBN: 978-3-030-86137-7
eBook Packages: Computer ScienceComputer Science (R0)