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Textual emotion recognition method based on ALBERT-BiLSTM model and SVM-NB classification

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Abstract

Textual emotion recognition is an increasingly popular research area, which recognizes human emotions by capturing textual information posted by people, and the recognition results depend on the composition of the system framework. In this paper, we propose a textual emotion recognition method based on ALBERT-BiLSTM model and SVM-NB classification. Firstly, the text is pre-processed based on ALBERT pre-training model. Then, the word vector-related features are obtained by BiLSTM Recurrent Neural Network for machine learning to make them have a specific form for classification in order to improve the accuracy of emotion recognition. In the link of emotion classification, this paper innovatively proposes a classification method SVM-NB to obtain more emotional polarities. Finally, the classifier is used to obtain the emotional polarities of the text, including positive and negative categories. The negative emotions are divided into three sub-categories of anger, sad and disgust. The experiments show that the proposed emotion recognition method has better robustness and higher accuracy than the general modal recognition method.

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Data availability

The datasets generated during and analyzed during the current study are not publicly available due to the paper involves the confidentiality of the research project, which will have a certain negative impact on the project team, but are available from the corresponding author on reasonable request.

References

  • Atmaja BT, Sasou A, Akagi M (2022) Survey on bimodal speech emotion recognition from acoustic and linguistic information fusion. Speech Commun 140:11–28. https://doi.org/10.1016/j.specom.2022.03.002

    Article  Google Scholar 

  • Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: Proceedings of the 3rd international conference on learning representations, ICLR 2015 - conference track proceedings, pp 1–15

  • Bashir MF, Javed AR, Arshad MU, Gadekallu TR, Shahzad W, Beg MO (2022) Context aware emotion detection from low resource urdu language using deep neural network. ACM Trans Asian Low-Resour Lang Inf Process 1:1–32. https://doi.org/10.1145/3528576

    Article  Google Scholar 

  • Chen R, Ren CG, Wang ZY, Qu ZJ, Wang HP (2019) Attention based CRNN for text classification. Comput Eng Des 40(11):3151–3157. https://doi.org/10.16208/j.issn1000-7024.2019.11.015

    Article  Google Scholar 

  • Cho K, Van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the EMNLP 2014 - 2014 conference on empirical methods in natural language processing, proceedings of the conference, pp 1724–1734

  • Devlin J, Chang MW, Lee K, Toutanova K (2019) Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the NAACL HLT 2019 - 2019 conference of the North American chapter of the association for computational linguistics: human language technologies - proceedings of the conference vol 1, pp. 4171–4186

  • Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18(5–6):602–610. https://doi.org/10.1016/j.neunet.2005.06.042

    Article  Google Scholar 

  • Huang CH, Yin J, Hou F (2011) A text similarity measurement combining word semantic information with TF-IDF method. Jisuanji Xuebao/Chin J Comput 34(5):856–864. https://doi.org/10.3724/SP.J.1016.2011.00856

    Article  Google Scholar 

  • Jin ZW, Cao J, Zhang YD, Zhou JS, Tian Q (2017) Novel visual and statistical image features for microblogs news verification. IEEE Trans Multimed 19(3):598–608. https://doi.org/10.1109/TMM.2016.2617078

    Article  Google Scholar 

  • Kim Y (2014) Convolutional neural network for sentence classification. In: Proceedings of the conference on empirical methods in natural language processing, pp 1–6

  • Lan ZZ, Chen MD, Goodman S, Gimpel K, Sharma P, Soricut R (2020) Albert: a lite bert for self-supervised learning of language representations. In: Proceedings of the international conference on learning representations, pp 1–17

  • Li ML, Qin L, Li WJ (2016) Emotion corpus construction based on selection from hashtags. In: Proceedings of the 10th international conference on language resources and evaluation, LREC 2016, pp 1845–1849

  • Lin ZH, Feng MW, Santos CND, Yu M, Xiang B, Zhou BW, Bengio Y (2017) A structured self-attentive sentence embedding. In: Proceedings of the 5th international conference on learning representations, ICLR 2017, pp 1–15

  • Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. In: Proceedings of the 1st international conference on learning representations, ICLR 2013 - Workshop Track Proceedings, pp 1–12

  • Qiu NJ, Cong L, Zhou SC, Wang P, Li YF (2019) SVD-CNN barrage text classification algorithm combined with improved active learning. J Comput Appl 39(3):644. https://doi.org/10.11772/j.issn.1001-9081.2018081757

    Article  Google Scholar 

  • Wang LX (2013) A literature review on pre-processing and learning of microtext. Library Inf Serv 57(11):125. https://doi.org/10.7536/j.jssn.0252-3116.2013.11.023

    Article  Google Scholar 

  • Wang Y, Xu SS, Li C, Ai SC, Zhang WD, Zhen L, Meng D (2020) Classification model based on support vector machine for Chinese extremely short text. Appl Res Comput 37(2):347–350. https://doi.org/10.19734/j.issn.1001-3695.2018.06.0514

    Article  Google Scholar 

  • Yang ZL, Dai ZH, Yang YM, Carbonell J, Salakhutdinov R, Le QV (2019) XLNet: generalized autoregressive pretraining for language understanding. Proc Adv Neural Inf Process Syst 32:1–18

    Google Scholar 

  • Yao YL, Wang SW, Xu RF, Liu B, Gui L, Lu Q, Wang XL (2014) The construction of an emotion annotated corpus on microblog text. J Chin Inf Process 28(5):83–91. https://doi.org/10.3969/j.issn.1003-0077.2014.05.011

    Article  Google Scholar 

  • Zahiri SM, Choi JD (2017) Emotion detection on TV show transcripts with sequence-based convolutional neural networks. arXiv preprint arXiv: 1708.04299

  • Zehra W, Javed AR, Jalil Z, Khan HU, Gadekallu TR (2021) Cross corpus multi-lingual speech emotion recognition using ensemble learning. Complex Intell Syst 7:1845–1854. https://doi.org/10.1007/s40747-020-00250-4

    Article  Google Scholar 

  • Zeng C, Wen CD, Sun YM, Pan L, He P (2021) Barrage text sentiment analysis based on ALBERT-CRNN. J Zhengzhou Univ Nat Sci Edn 53(03):1–8. https://doi.org/10.13705/j.issn.1671-6841.2020359

    Article  Google Scholar 

  • Zhang LL (2011) On the two kinds of definition of likelihood function. Value Eng 30(30):1. https://doi.org/10.14018/j.cnki.cn13-1085/n.2011.30.006

    Article  Google Scholar 

  • Zhang JW, Cui LM, Fu YJ, Gouza FB (2018) FAKEDETECTOR: effective fake news detection with deep diffusive neural network. arXiv preprint arXiv:1805.08751

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Funding

The work was supported by Hubei Technological Innovation Special Fund, under Grant 2019AAA071, the project of National Natural Science Foundation of China under Grant No. 62073249.

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Authors and Affiliations

  1. College of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China

    Zihao Ye, Tao Zuo, Waner Chen, Yingxin Li & Ziyu Lu

  2. Engineering Research Center for Metallurgical Automation and Detecting Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, China

    Zihao Ye & Tao Zuo

Authors
  1. Zihao Ye
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  2. Tao Zuo
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  3. Waner Chen
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  4. Yingxin Li
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  5. Ziyu Lu
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Contributions

ZY, TZ and YL contributed to the study conception and design. Material preparation was performed by ZY, TZ and WC. Data collection and analysis were performed by ZY and ZL. The program code was written by ZY. The first draft of the manuscript was written by ZY and TZ. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Tao Zuo.

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This manuscript does not contain any studies with human participants or animals performed by any of the authors. We have read and have abided by the statement of ethical standards for manuscripts.

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Ye, Z., Zuo, T., Chen, W. et al. Textual emotion recognition method based on ALBERT-BiLSTM model and SVM-NB classification. Soft Comput 27, 5063–5075 (2023). https://doi.org/10.1007/s00500-023-07924-4

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