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.
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This research was funded by the Shandong Provincial Natural Science Foundation under Project ZR2019MF034.
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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
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DOI: https://doi.org/10.1007/978-3-030-86137-7_23
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