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Text Sentiment Analysis Based on Multi-Layer Bi-Directional LSTM with a Trapezoidal Structure

Zhengfang He1,2,*, Cristina E. Dumdumaya2, Ivy Kim D. Machica2

1 School of Intelligent Science and Engineering, Yunnan Technology and Business University, Kunming, 650000, China
2 College of Information and Computing, University of Southeastern Philippines, Davao City, Davao del Sur, Philippines

* Corresponding Author: Zhengfang He. Email: email

Intelligent Automation & Soft Computing 2023, 37(1), 639-654. https://doi.org/10.32604/iasc.2023.035352

Abstract

Sentiment analysis, commonly called opinion mining or emotion artificial intelligence (AI), employs biometrics, computational linguistics, natural language processing, and text analysis to systematically identify, extract, measure, and investigate affective states and subjective data. Sentiment analysis algorithms include emotion lexicon, traditional machine learning, and deep learning. In the text sentiment analysis algorithm based on a neural network, multi-layer Bi-directional long short-term memory (LSTM) is widely used, but the parameter amount of this model is too huge. Hence, this paper proposes a Bi-directional LSTM with a trapezoidal structure model. The design of the trapezoidal structure is derived from classic neural networks, such as LeNet-5 and AlexNet. These classic models have trapezoidal-like structures, and these structures have achieved success in the field of deep learning. There are two benefits to using the Bi-directional LSTM with a trapezoidal structure. One is that compared with the single-layer configuration, using the of the multi-layer structure can better extract the high-dimensional features of the text. Another is that using the trapezoidal structure can reduce the model’s parameters. This paper introduces the Bi-directional LSTM with a trapezoidal structure model in detail and uses Stanford sentiment treebank 2 (STS-2) for experiments. It can be seen from the experimental results that the trapezoidal structure model and the normal structure model have similar performances. However, the trapezoidal structure model parameters are 35.75% less than the normal structure model.

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Cite This Article

APA Style
He, Z., Dumdumaya, C.E., Machica, I.K.D. (2023). Text sentiment analysis based on multi-layer bi-directional LSTM with a trapezoidal structure. Intelligent Automation & Soft Computing, 37(1), 639-654. https://doi.org/10.32604/iasc.2023.035352
Vancouver Style
He Z, Dumdumaya CE, Machica IKD. Text sentiment analysis based on multi-layer bi-directional LSTM with a trapezoidal structure. Intell Automat Soft Comput . 2023;37(1):639-654 https://doi.org/10.32604/iasc.2023.035352
IEEE Style
Z. He, C.E. Dumdumaya, and I.K.D. Machica "Text Sentiment Analysis Based on Multi-Layer Bi-Directional LSTM with a Trapezoidal Structure," Intell. Automat. Soft Comput. , vol. 37, no. 1, pp. 639-654. 2023. https://doi.org/10.32604/iasc.2023.035352



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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