Attention and LoRA-based multimodal emotion detection system.
In the field of sentiment analysis, understanding the complex range of human emotions from product reviews presents a formidable challenge, especially when considering the multimodal nature of contemporary datasets. This paper introduces a new approach to emotion detection by using the synergistic p...
| Publicado en: | Language Resources & Evaluation Vol. 59; no. 3; pp. 2929 - 2945 |
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| Autores principales: | , |
| Formato: | Conference Paper/Materials |
| Publicado: |
Springer Nature
Sep2025
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| 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=186909095&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 186909095 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2025 vid: 59 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 186909095 10.1007/s10579-025-09841-4 ppf: 2929 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.7MB tig: atl: Attention and LoRA-based multimodal emotion detection system. aug: au: Gorai, Joy Shaw, Dilip Kumar affil: https://ror.org/01sebzx27 Department of Computer Science and Engineering, National Institute of Technology, Jamshedpur, India su: Emotion recognition Sentiment analysis Amazon.com Inc. Product reviews Detection algorithms Long short-term memory Convolutional neural networks Machine learning sug: subj: Emotion recognition Sentiment analysis Amazon.com Inc. Product reviews Detection algorithms Long short-term memory Convolutional neural networks Machine learning keyword: Attention Emotion detection Information and Computing Sciences Artificial Intelligence and Image Processing Low rank adaptation Multimodal Product review ab: In the field of sentiment analysis, understanding the complex range of human emotions from product reviews presents a formidable challenge, especially when considering the multimodal nature of contemporary datasets. This paper introduces a new approach to emotion detection by using the synergistic potential of text and images through an attention-based multimodal system. Our method employs a Bidirectional Long Short-Term Memory (BiLSTM) model combined with an attention mechanism to intricately analyze textual data alongside multiple Convolutional Neural Networks (CNNs) to process image data, effectively capturing the emotional undertones conveyed through visual content. LoRA (Low-Rank Adaptation) is used to minimize the huge parameter computations on the image and text concatenation. Uniquely, this study focuses on a dataset curated from Amazon reviews, a domain where little or no prior research has been done to detect emotion from multimedia data. By thoroughly compiling this dataset, a significant resource gap in this field could be lessened. Our results demonstrate that the proposed attention-based BiLSTM and CNN framework significantly outperforms existing models, offering deeper insights into consumer emotions beyond the traditional binary sentiment classification. This advancement underscores the importance of integrating multiple data modalities for a comprehensive understanding of consumer feedback and opens new avenues for research in emotion detection within product reviews. pubtype: Academic Journal doctype: Conference Paper/Materials src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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