Aspect-Based Sentiment Analysis of Customer Speech Data Using Deep Convolutional Neural Network and BiLSTM

Khalid HM, Helander MG. Customer emotional needs in product design. Concurr Eng. 2006;14(3):197–206. https://doi.org/10.1177/1063293X06068387.

Article  Google Scholar 

Fu Y, Liao J, Li Y, Wang S, Li D, Li X. Multiple perspective attention based on double BiLSTM for aspect and sentiment pair extract. Neurocomputing. 2021;438:302–11. https://doi.org/10.1016/j.neucom.2021.01.079.

Article  Google Scholar 

Li G, Liu F, Wang Y, Guo Y, Xiao L, Zhu L. A convolutional neural network (CNN) based approach for the recognition and evaluation of classroom teaching behavior. Sci Program. 2021;2021:8. https://doi.org/10.1155/2021/6336773.

Article  Google Scholar 

Lu Z, Cao L, Zhang Y, Chiu CC, Fan J. Speech sentiment analysis via pre-trained features from end-to-end asr models. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE; 2020. p. 7149–53. https://doi.org/10.1109/ICASSP40776.2020.9052937.

Chapter  Google Scholar 

Capuano N, Greco L, Ritrovato P, Vento M. Sentiment analysis for customer relationship management: an incremental learning approach. Appl Intell. 2021;51(6):3339–52. https://doi.org/10.1007/s10489-020-01984-x.

Article  Google Scholar 

Yadav S, Ekbal A, Saha S, Bhattacharyya P. Medical sentiment analysis using social media: towards building a patient assisted system. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). ELRA; 2018. p. 2790–7.

Google Scholar 

Das RK, Panda M, Misra H. Decision support grievance redressal system using sentence sentiment analysis. In: Proceedings of the 13th International Conference on Theory and Practice of Electronic Governance. Association for Computing Machinery; 2020. p. 17–24. https://doi.org/10.1145/3428502.3428505.

Chapter  Google Scholar 

Maghilnan S, Kumar MR. Sentiment analysis on speaker specific speech data. In: 2017 international conference on intelligent computing and control (I2C2). IEEE; 2017. p. 1–5. https://doi.org/10.1109/I2C2.2017.8321795.

Chapter  Google Scholar 

Ezzat S, El Gayar N, Ghanem MM. Sentiment analysis of call centre audio conversations using text classification. Int J Comput Inf Syst Ind Manag Appl. 2012;4(1):619–27.

Google Scholar 

Lakomkin E, Zamani MA, Weber C, Magg S, Wermter S. Incorporating end-to-end speech recognition models for sentiment analysis. In: 2019 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2019. p. 7976–82. https://doi.org/10.1109/ICRA.2019.8794468.

Chapter  Google Scholar 

Huang Z, Dong M, Mao Q, Zhan Y. Speech emotion recognition using CNN. Proceedings of the 22nd ACM international conference on Multimedia; 2014. p. 801–4. https://doi.org/10.1145/2647868.2654984.

Book  Google Scholar 

Haq S, Jackson PJB. Speaker-dependent audio-visual emotion recognition. Proc. Int. Conf. on Auditory-Visual Speech Processing (AVSP’09); 2009. p. 1–6.

Google Scholar 

Berlin TU, Science C, Berlin LKA, Berlin HU. A database of German emotional speech. Proceedings Interspeech; 2005. https://doi.org/10.21437/Interspeech.2005-446.

Book  MATH  Google Scholar 

Ververidis D, Kotropoulos C, Pitas I. Automatic emotional speech classification. In: 2004 IEEE international conference on acoustics, speech, and signal processing, vol. 1. IEEE; 2004. p. 1–593. https://doi.org/10.1109/ICASSP.2004.1326055.

Chapter  Google Scholar 

Cui C, Ren Y, Liu J, Chen F, Huang R, Lei M, Zhao Z. EMOVIE: a Mandarin emotion speech dataset with a simple emotional text-to-speech model. Interspeech, pp. 1-5. 2021. https://doi.org/10.21437/Interspeech.2021-1148.

Han K, Yu D, Tashev I. Speech emotion recognition using deep neural network and extreme learning machine. Interspeech; 2014. p. 223–7. https://doi.org/10.21437/Interspeech.2014-57.

Book  Google Scholar 

M. Xu, F. Zhang and W. Zhang, Head Fusion: Improving the Accuracy and Robustness of Speech Emotion Recognition on the IEMOCAP and RAVDESS Dataset, IEEE Access, 9, pp. 74539-74549, 2021, https://doi.org/10.1109/ACCESS.2021.3067460.

Article  Google Scholar 

Mirsamadi S, Barsoum E, Zhang C. Automatic speech emotion recognition using recurrent neural networks with local attention. In: 2017 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE; 2017. p. 2227–31. https://doi.org/10.1109/ICASSP.2017.7952552.

Chapter  Google Scholar 

Chen M, He X, Yang J, Zhang H. 3-D convolutional recurrent neural networks with attention model for speech emotion recognition. IEEE Signal Process Lett. 2018;25(10):1440–4. https://doi.org/10.1109/LSP.2018.2860246.

Article  Google Scholar 

Xie Y, Liang R, Liang Z, Huang C, Zou C, Schuller B. Speech emotion classification using attention-based LSTM. IEEE/ACM Trans Audio Speech Lang Process. 2019;27(11):1675–85. https://doi.org/10.1109/TASLP.2019.2925934.

Article  Google Scholar 

Zhao J, Mao X, Chen L. Speech emotion recognition using deep 1D and 2D CNN LSTM networks. Biomed Signal Process Control. 2019;47:312–23. https://doi.org/10.1016/j.bspc.2018.08.035.

Article  Google Scholar 

Sajjad M, Kwon S. Clustering-based speech emotion recognition by incorporating learned features and deep BiLSTM. IEEE Access. 2020;8:79861–75. https://doi.org/10.1109/ACCESS.2020.2990405.

Article  Google Scholar 

Kwon S. A CNN-assisted enhanced audio signal processing for speech emotion recognition. Sensors. 2019;20(1):183. https://doi.org/10.3390/s20010183.

Article  Google Scholar 

Issa D, Demirci MF, Yazici A. Speech emotion recognition with deep convolutional neural networks. Biomed Signal Process Control. 2020;59:101894. https://doi.org/10.1016/j.bspc.2020.101894.

Article  Google Scholar 

Atila O, Şengür A. Attention guided 3D CNN-LSTM model for accurate speech based emotion recognition. Appl Acoust. 2021;182:108260. https://doi.org/10.1016/j.apacoust.2021.108260.

Article  Google Scholar 

Kwon S. CLSTM: deep feature-based speech emotion recognition using the hierarchical ConvLSTM network. Mathematics. 2020;8(12):2133. https://doi.org/10.3390/math8122133.

Article  Google Scholar 

Chiril P, Pamungkas EW, Benamara F, Moriceau V, Patti V. Emotionally informed hate speech detection: a multi-target perspective. Cogn Comput. 2022;14(1):322–52. https://doi.org/10.1007/s12559-021-09862-5.

Article  Google Scholar 

Chatziagapi A, Paraskevopoulos G, Sgouropoulos D, Pantazopoulos G, Nikandrou M, Giannakopoulos T, Narayanan S. Data augmentation using GANs for speech emotion recognition. Interspeech; 2019. p. 171–5. https://doi.org/10.21437/Interspeech.2019-2561.

Book  Google Scholar 

Wu JJ, Chang ST. Exploring customer sentiment regarding online retail services: a topic-based approach. J Retail Consum Serv. 2020;55:102145. https://doi.org/10.1016/j.jretconser.2020.102145.

Article  Google Scholar 

McFee B, Raffel C, Liang D, Ellis DPW, McVicar M, Battenberg E, Nieto O. librosa: audio and music signal analysis in python. Proceedings of the 14th python in science conference; 2015. p. 18–25. https://doi.org/10.25080/Majora-7b98e3ed-003.

Book  Google Scholar 

Alim SA, Rashid NKA. Some commonly used speech feature extraction algorithms. IntechOpen; 2018. p. 2–19. https://doi.org/10.5772/intechopen.80419.

Book  Google Scholar 

Shashidhar R, Patilkulkarni S. Audiovisual speech recognition for Kannada language using feed forward neural network. Neural Comput Appl. 2022;34:15603–15. https://doi.org/10.1007/s00521-022-07249-7.

Article  Google Scholar 

Pawar MD, Kokate RD. Convolution neural network based automatic speech emotion recognition using Mel-frequency Cepstrum coefficients. Multimed Tools Appl. 2021;80(10):15563–87. https://doi.org/10.1007/s11042-020-10329-2.

Article  Google Scholar 

Gomathy M. Optimal feature selection for speech emotion recognition using enhanced cat swarm optimization algorithm. Int J Speech Technol. 2021;24(1):155–63. https://doi.org/10.1007/s10772-020-09776-x.

Article  Google Scholar 

Sainath TN, Kingsbury B, Saon G, Soltau H, Mohamed AR, Dahl G, Ramabhadran B. Deep convolutional neural networks for large-scale speech tasks. Neural Netw. 2015;64:39–48. https://doi.org/10.1016/j.neunet.2014.08.005.

Article  Google Scholar 

Kingma DP, Ba J. Adam: a method for stochastic optimization. International Conference for Learning Representations, pp. 1-15. 2015. arXiv:1412.6980. https://doi.org/10.48550/arXiv.1412.6980.

Sutskever I, Martens J, Dahl G, Hinton G. On the importance of initialization and momentum in deep learning. In: Proc. Int. Conf. Mach. Learn. PMLR; 2013. p. 1139–47.

Google Scholar 

Duchi J, Hazan E, Singer Y. Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res. 2011;12(61):2121–59.

MathSciNet  MATH  Google Scholar 

Zeiler M. ADADELTA: An Adaptive Learning Rate Method. ArXiv, abs/1212.5701. 2012. https://doi.org/10.48550/arXiv.1212.5701.

Xu D, Zhang S, Zhang H, Mandic DP. Convergence of the RMSProp deep learning method with penalty for nonconvex optimization. Neural Netw. 2021;139:17–23. https://doi.org/10.1016/j.neunet.2021.02.011.

Article  Google Scholar 

Kimura T, Nose T, Hirooka S, Chiba Y, Ito A. Comparison of speech recognition performance between Kaldi and Google Cloud Speech API. In: Pan JS, Ito A, Tsai PW, Jain L, editors. Recent advances in intelligent information hiding and multimedia signal processing. IIH-MSP 2018. Smart Innovation, Systems and Technologies, vol. 110. Cham: Springer; 2019. https://doi.org/10.1007/978-3-030-03748-2_13.

Chapter  Google Scholar 

Iancu B. Evaluating google speech-to-text API’s performance for Romanian e-learning resources. Inf Econ. 2019;23(1):17–25. https://doi.org/10.12948/ISSN14531305/23.1.2019.02.

Article  Google Scholar 

Wang X, Liu Y, Sun C, Liu M, Wang X. Extended dependency-based word embeddings for aspect extraction. In: International Conference on Neural Information Processing. Springer; 2016. p. 104–11. https://doi.org/10.1007/978-3-319-46681-1_13.

Chapter  Google Scholar 

Sharma AK, Chaurasia S, Srivastava DK. Sentimental short sentences classification by using CNN deep learning model with fine tuned Word2Vec. Procedia Comput Sci. 2020;167:1139–47. https://doi.org/10.1016/j.procs.2020.03.416.

Article  Google Scholar 

Patilkulkarni S. Visual speech recognition for small scale dataset using VGG16 convolution neural network. Multimed Tools Appl. 2021;80(19):28941–52. https://doi.org/10.1007/s11042-021-11119-0.

Article  Google Scholar 

Livingstone SR, Russo FA. The Ryerson audio-visual database of emotional speech and song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLOS ONE. 2018;13(5). https://doi.org/10.1371/journal.pone.0196391.

Haq S, Jackson PJB. Speaker-dependent audio-visual emotion recognition. Proc. Int’l Conf. on Auditory-Visual Speech Processing; 2009. p. 53–8.

Google Scholar 

Berlin TU, Science C, Berlin LKA, Berlin HU. A database of German emotional speech. Interspeech. 2005;5:1517–20.

Google Scholar 

Busso C, Bulut ÆM, Abe ÆCLÆ, Mower E, Kim ÆS, Chang ÆJN, et al. IEMOCAP: interactive emotional dyadic motion capture database. Lang Resour Eval. 2018;42:335–59. https://doi.org/10.1007/s10579-008-9076-6.

Article  Google Scholar 

Shashidhar R, Patilkulkarni S, Puneeth SB. Combining audio and visual speech recognition using LSTM and deep convolutional neural network. Int J Inf Technol. 2022;14(7):3425–36. https://doi.org/10.1007/s41870-022-00907-y.

Article  Google Scholar 

Srividya K, Sowjanya AM. Aspect based sentiment analysis using RNN-LSTM. Int J Adv Sci Technol. 2020;29(4):5875–80.

Google Scholar 

Al-Smadi M, Talafha B, Al-Ayyoub M, Jararweh Y. Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews. Int J Mach Learn Cybern. 2019;10(8):2163–75. https://doi.org/10.1007/s13042-018-0799-4.

Article  Google Scholar 

Xu L, Lin J, Wang L, Yin C, Wang J. Deep convolutional neural network based approach for aspect-based sentiment analysis. Adv Sci Technol Lett. 2017;143:199–204. https://doi.org/10.14257/ASTL.2017.143.41.

Article  Google Scholar 

Kumar R, Pannu HS, Malhi AK. Aspect-based sentiment analysis using deep networks and stochastic optimization. Neural Comput Appl. 2020;32(8):3221–35. https://doi.org/10.1007/s00521-019-04105-z.

留言 (0)

沒有登入
gif