CONTEXT-AWARE SENTIMENT ANALYSIS: A MULTIMODAL APPROACH FOR IMPROVED EMOTION RECOGNITION.
Sentiment analysis has come a long way since it was only about classifying text. Now it uses visuals, audio, and contextual signals to provide a better and more accurate picture of how people feel. This study shows a sentiment analysis approach that takes into account the context and uses multimodal...
| Publicado en: | Scientific Culture Vol. 12; no. 2, Part 1; pp. 2560 - 2575 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | Artículo |
| Publicado: |
University of the Aegean
2026
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=191995900&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 191995900 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 24080071 I6HU jtl: Scientific Culture issn: 24080071 maglogo: N pubinfo: dt: 2026 vid: 12 iid: 2, Part 1 pid: 47715 pub: University of the Aegean artinfo: ui: 191995900 10.5281/zenodo.122.126198 ppf: 2560 ppct: 15 formats: tig: atl: CONTEXT-AWARE SENTIMENT ANALYSIS: A MULTIMODAL APPROACH FOR IMPROVED EMOTION RECOGNITION. aug: au: Durga, Putta Godavarthi, Deepthi Mohanty, Sachi Nandan Ahmed, Mohammed Altaf Alnatheer, Suleman Mohammed, Qutubuddin affil: School of Computer Science and Engineering, VIT-AP University, Amaravati, India. Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Saudi Arabia. Department of Electrical Engineering, College of Engineering Wadi Addawasir, Prince Sattam bin Abdulaziz University, Saudi Arabia. su: Convolutional neural networks Deep learning Affective computing Language models Nonnegative matrices sug: subj: Convolutional neural networks Deep learning Affective computing Language models Nonnegative matrices keyword: Cybersecurity EfficientNet-B3 for Image Sentiment Analysis Emotion Recognition Multimodal Sentiment Analysis Social Media Analysis Sustainable Environment Transformer Models ab: Sentiment analysis has come a long way since it was only about classifying text. Now it uses visuals, audio, and contextual signals to provide a better and more accurate picture of how people feel. This study shows a sentiment analysis approach that takes into account the context and uses multimodal learning to improve emotion recognition. The suggested method uses powerful deep learning methods to extract features and classify them across different types of data. Deep learning-based word embeddings like BERT and FastText are used for textual sentiment analysis to get rich linguistic representations and contextual dependencies. We also employ Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorisation (NMF) to find underlying topic structures and pull out useful latent characteristics from text data. For sentiment analysis based on images, we use EfficientNet-B3 because it is better at extracting features and is faster at doing so. Also, hybrid convolutional neural networks (CNNs) are used for both text and image-based emotion recognition, which makes the sentiment analysis technique more complete. The system is thoroughly tested on benchmark datasets, and the results show that it is far better than unimodal sentiment analysis methods. The Hybrid CNN model was able to tell how someone felt about a piece of text with 92.3% accuracy just looking at the text itself. The EfficientNet-B3 model, which is based on images, has an accuracy of 96.7%. This shows that it can pick up on emotional cues from visual data and that deep learning is useful for image-based sentiment analysis. Multimodal context improves the accuracy of categorisation, which makes it beneficial for social media analysis, AI that can understand emotions, and initiating research on sentiment and emotion detection. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
|---|