Analyzing sentiment and its application in deep learning: Consistent behavior across multiple occasions

Research Article
Open access

Analyzing sentiment and its application in deep learning: Consistent behavior across multiple occasions

Yanxiong Xiang 1*
  • 1 School of Computer Science, South China Business College Guangdong University of Foreign Studies, Guangzhou, 510545, China    
  • *corresponding author 1840606237@e.gwng.edu.cn
Published on 4 February 2024 | https://doi.org/10.54254/2755-2721/33/20230226
ACE Vol.33
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-291-6
ISBN (Online): 978-1-83558-292-3

Abstract

This article offers a systematic review of the evolution in sentiment analysis techniques, moving from unimodal to multimodal to multi-occasion methodologies, with an emphasis on the integration and application of deep learning in sentiment analysis. Firstly, the paper presents the theoretical foundation of sentiment analysis, including the definition and classification of affect and emotion. It then delves into the pivotal technologies used in unimodal sentiment analysis, specifically within the domains of text, speech, and image analysis, examining feature extraction, representation, and classification models. Subsequently, the focus shifts to multimodal sentiment analysis. The paper offers a survey of widely utilized multimodal sentiment datasets, feature representation and fusion techniques, as well as deep learning-based multimodal sentiment analysis models such as attention networks and graph neural networks. It further addresses the application of these multimodal sentiment analysis techniques in social media, product reviews, and public opinion monitoring. Lastly, the paper underscores that challenges persist in the area of multimodal sentiment fusion, including data imbalance and disparities in feature expression. It calls for further research into cross-modal feature expression, dataset augmentation, and explainable modeling to enhance the performance of complex sentiment analysis across multiple occasions.

Keywords:

artificial intelligence, sentiment analysis, deep learning, application scenarios

Xiang,Y. (2024). Analyzing sentiment and its application in deep learning: Consistent behavior across multiple occasions. Applied and Computational Engineering,33,18-27.
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References

[1]. Hu, M., & Chen, S. (2019). One-Pass Incomplete Multi-View Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 3838-3845.

[2]. Abdullah, T., & Ahmet, A. (2023). Deep Learning in Sentiment Analysis: Recent Architectures. ACM Computing Surveys, 55(8), Article 159.

[3]. Chan, J.YL., Bea, K.T., Leow, S.M.H. et al. (2023). State of the art: a review of sentiment analysis based on sequential transfer learning. Artificial Intelligence Review, 56, 749–780.

[4]. WANG, Y., ZHU, J., WANG, Z., BAI, F., & GONG, J. (2022). Review of applications of natural language processing in text sentiment analysis. Journal of Computer Applications, 42(4), 1011-1020.

[5]. Atila, O., Şengür, A. (2021). Attention guided 3D CNN-LSTM model for accurate speech based emotion recognition. Applied Acoustics, 182, 108260.

[6]. Kumar, V. S., Pareek, P. K., Costa de Albuquerque, V. H., Khanna, A., Gupta, D., & S, D. R. (2022). Multimodal Sentiment Analysis using Speech Signals with Machine Learning Techniques. 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), 1-8.

[7]. Abdaoui, A., Pradel, C., & Sigel, G. (2020). Load What You Need: Smaller Versions of Multilingual BERT. Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing, 119–123.

[8]. Mocanu, B., Tapu, R., & Zaharia, T. (2023). Multimodal emotion recognition using cross modal audio-video fusion with attention and deep metric learning. Image and Vision Computing, 133, 104676.

[9]. Middya, A. I., Nag, B., & Roy, S. (2022). Deep learning based multimodal emotion recognition using model-level fusion of audio–visual modalities. Knowledge-Based Systems, 244, 108580.

[10]. Song, T., Zhang, X., Ding, M., Rodriguez-Paton, A., Wang, S., & Wang, G. (2022). DeepFusion: A deep learning based multi-scale feature fusion method for predicting drug-target interactions. Methods, 204, 269-277.

[11]. Moshayedi, A. J., Roy, A. S., Kolahdooz, A., & Shuxin, Y. (2022). Deep Learning Application Pros And Cons Over Algorithm. AIRO, EAI.

[12]. Jain, P.K., Quamer, W., Saravanan, V. et al. (2023). Employing BERT-DCNN with sentic knowledge base for social media sentiment analysis. Journal of Ambient Intelligence and Humanized Computing, 14, 10417–10429.

[13]. D’Aniello, G., Gaeta, M., & La Rocca, I. (2022). KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis. Artificial Intelligence Review, 55, 5543–5574.

[14]. Gandhi, A., Adhvaryu, K., Poria, S., Cambria, E., & Hussain, A. (2023). Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions. Information Fusion, 91, 424-444.

[15]. Wankhade, M., Rao, A.C.S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55, 5731–578.

Cite this article

Xiang,Y. (2024). Analyzing sentiment and its application in deep learning: Consistent behavior across multiple occasions. Applied and Computational Engineering,33,18-27.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-291-6(Print) / 978-1-83558-292-3(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.33
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Hu, M., & Chen, S. (2019). One-Pass Incomplete Multi-View Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 3838-3845.

[2]. Abdullah, T., & Ahmet, A. (2023). Deep Learning in Sentiment Analysis: Recent Architectures. ACM Computing Surveys, 55(8), Article 159.

[3]. Chan, J.YL., Bea, K.T., Leow, S.M.H. et al. (2023). State of the art: a review of sentiment analysis based on sequential transfer learning. Artificial Intelligence Review, 56, 749–780.

[4]. WANG, Y., ZHU, J., WANG, Z., BAI, F., & GONG, J. (2022). Review of applications of natural language processing in text sentiment analysis. Journal of Computer Applications, 42(4), 1011-1020.

[5]. Atila, O., Şengür, A. (2021). Attention guided 3D CNN-LSTM model for accurate speech based emotion recognition. Applied Acoustics, 182, 108260.

[6]. Kumar, V. S., Pareek, P. K., Costa de Albuquerque, V. H., Khanna, A., Gupta, D., & S, D. R. (2022). Multimodal Sentiment Analysis using Speech Signals with Machine Learning Techniques. 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), 1-8.

[7]. Abdaoui, A., Pradel, C., & Sigel, G. (2020). Load What You Need: Smaller Versions of Multilingual BERT. Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing, 119–123.

[8]. Mocanu, B., Tapu, R., & Zaharia, T. (2023). Multimodal emotion recognition using cross modal audio-video fusion with attention and deep metric learning. Image and Vision Computing, 133, 104676.

[9]. Middya, A. I., Nag, B., & Roy, S. (2022). Deep learning based multimodal emotion recognition using model-level fusion of audio–visual modalities. Knowledge-Based Systems, 244, 108580.

[10]. Song, T., Zhang, X., Ding, M., Rodriguez-Paton, A., Wang, S., & Wang, G. (2022). DeepFusion: A deep learning based multi-scale feature fusion method for predicting drug-target interactions. Methods, 204, 269-277.

[11]. Moshayedi, A. J., Roy, A. S., Kolahdooz, A., & Shuxin, Y. (2022). Deep Learning Application Pros And Cons Over Algorithm. AIRO, EAI.

[12]. Jain, P.K., Quamer, W., Saravanan, V. et al. (2023). Employing BERT-DCNN with sentic knowledge base for social media sentiment analysis. Journal of Ambient Intelligence and Humanized Computing, 14, 10417–10429.

[13]. D’Aniello, G., Gaeta, M., & La Rocca, I. (2022). KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis. Artificial Intelligence Review, 55, 5543–5574.

[14]. Gandhi, A., Adhvaryu, K., Poria, S., Cambria, E., & Hussain, A. (2023). Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions. Information Fusion, 91, 424-444.

[15]. Wankhade, M., Rao, A.C.S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55, 5731–578.

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