Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Attention and LoRA-based multimodal emotion detection system

  • 期刊论文

  • 发表日期：

    2025年5月21日

查看全文
  • Joy Gorai,
  • Dilip Kumar Shaw

摘要

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.