Deep learning-based sentiment analysis for social media: A focus on multimodal and aspect-based approaches

Bowen Feng

doi:10.54254/2755-2721/33/20230224

Research Article

Open access

Published on 4 February 2024

Download pdf

Feng,B. (2024). Deep learning-based sentiment analysis for social media: A focus on multimodal and aspect-based approaches. Applied and Computational Engineering,33,1-8.

Export citation

Deep learning-based sentiment analysis for social media: A focus on multimodal and aspect-based approaches

Bowen Feng *^,1,

¹ Li Jia high school, Chongqing, 401122, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/33/20230224

Abstract

Commonly referred to as opinion mining, sentiment analysis harnesses the power of deep learning systems to discern human emotions and subjective sentiments towards a wide array of subjects. As such, it has become an integral tool in identifying and distinguishing sentences that harbor emotional biases or trends. By systematically examining sentiment-tinged data, researchers can unearth pivotal insights that not only reflect current perspectives but also predict future behaviors and trends. This process involves intricate computational models that analyze and interpret the emotional undertones embedded within a body of text. Whether these undertones are positive, negative, or neutral, sentiment analysis allows us to delve into the subtle nuances of human communication. This ability to "understand" and quantify sentiment is particularly vital in our modern digital age, where opinions and reviews shared through social media and online platforms can greatly influence public sentiment and consumer behavior. By extending beyond the literal meanings of words and phrases, sentiment analysis can provide a more comprehensive understanding of how people truly feel. It is instrumental in fields as diverse as marketing, politics, social science, and even artificial intelligence development, given its potential to gauge public opinion and predict societal trends. This paper aims to consolidate relevant research within the field of sentiment analysis conducted in recent years. Furthermore, it seeks to prognosticate the future trajectories and impacts of this rapidly evolving domain. Emphasis is placed on the role of deep learning and its transformational effects on the approach and capabilities of sentiment analysis, anticipating how its further advancement will continue to refine this intricate process of emotion recognition and interpretation.

Keywords

deep learning, sentiment analysis, social media

View pdf

References

[1]. Li, W., Mei, H., Li, Y., et al. (2022). Survey of Multimodal Sentiment Analysis Based on Deep Learning.

[2]. Kim, Y., et al. (2014). Convolutional Neural Networks for Sentence Classification. arXiv:1408.5882v2.

[3]. Johnson, R., & Zhang, T. (2017). Deep pyramid convolutional neural networks for text categorization. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 562-570.

[4]. Li, H., Lin, Z., Shen, X., Brandt, J., & Hua, G. (2015). A convolutional neural network cascade for face detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5325-5334.

[5]. He, Z., Dumdumaya, C., & Machica, I. K. D. et al. (2023). Text Sentiment Analysis Based on Multi-Layer Bi-Directional LSTM with a Trapezoidal Structure. Intelligent Automation & Soft Computing, 37(1), 639-654.

[6]. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014). Going deeper with convolutions. arXiv:1409.4842v1.

[7]. Huang, F., Zhang, X., Zhao, Z., et al. (2019). Image-text sentiment analysis via deep multimodal attentive fusion. Knowledge-Based Systems, 167, 26-37.

[8]. Liao, W., Zeng, B., Liu, J., Wei, P., & Fang, J. (2021). Image-text interaction graph neural network for image-text sentiment analysis.

[9]. Xiao, Y., et al. (2022). Research on Emotional Analysis of Annotated Short Videos Based on Vision and Text.

[10]. Li, Y., Song, Y., Cao, L., et al. (2016). TGIF: A New Dataset and Benchmark on Animated GIF Description. IEEE.

[11]. Zadeh, A., Zellers, R., Pincus, E., et al. (2016). MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos.

[12]. Zadeh, A., Liang, P., Poria, S., et al. (2018). Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph. Thirty-Second AAAI Conference on Artificial Intelligence. ACL.

[13]. Hussain, M. D. S., Calvo, R. A., & Pour, P. A. (n.d.). Hybrid fusion approach for detecting affects from multichannel physiology. International Conference on Affective Computing and Intelligent Interaction. Springer, Berlin: Heidelberg.

[14]. Xiao, Y., et al. (2022). Sentiment Recognition for Annotated Short Videos Based on Vision and Text.

[15]. Yang, Y. (2022). Multimodal Emotion Analysis based on EEG and facial expression images of deaf students.

[16]. Tao, F., Wang, H., Lin, K., & Liu, Y. (2022). A Probe into Netizen Sentiment Analysis in Vision-based Internet Public Opinion Events.

[17]. Ghosh, A., Dhara, B. C., Pero, C., & Umer, S. (2023). A multimodal sentiment analysis system for recognizing person aggressiveness in pain based on textual and visual information. Journal of Ambient Intelligence and Humanized Computing, 14, 4489–4501.

Cite this article

Feng,B. (2024). Deep learning-based sentiment analysis for social media: A focus on multimodal and aspect-based approaches. Applied and Computational Engineering,33,1-8.

Data availability

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

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

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

Conference website: https://2023.confmla.org/

ISBN：978-1-83558-291-6(Print) / 978-1-83558-292-3(Online)

Conference date: 18 October 2023

Editor：Mustafa İSTANBULLU

Series: Applied and Computational Engineering

Volume number: Vol.33

ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).