Enriching Multimodal Sentiment Analysis through Textual Emotional Descriptions of Visual-Audio Content

• URL: http://arxiv.org/abs/2412.10460v1
• Date: Thu, 12 Dec 2024 11:30:41 GMT
• Title: Enriching Multimodal Sentiment Analysis through Textual Emotional Descriptions of Visual-Audio Content
• Authors: Sheng Wu, Xiaobao Wang, Longbiao Wang, Dongxiao He, Jianwu Dang,
• Abstract summary: Multimodal Sentiment Analysis seeks to unravel human emotions by amalgamating text, audio, and visual data. Yet, discerning subtle emotional nuances within audio and video expressions poses a formidable challenge. We introduce DEVA, a progressive fusion framework founded on textual sentiment descriptions.
• Score: 56.62027582702816
• License:
• Abstract: Multimodal Sentiment Analysis (MSA) stands as a critical research frontier, seeking to comprehensively unravel human emotions by amalgamating text, audio, and visual data. Yet, discerning subtle emotional nuances within audio and video expressions poses a formidable challenge, particularly when emotional polarities across various segments appear similar. In this paper, our objective is to spotlight emotion-relevant attributes of audio and visual modalities to facilitate multimodal fusion in the context of nuanced emotional shifts in visual-audio scenarios. To this end, we introduce DEVA, a progressive fusion framework founded on textual sentiment descriptions aimed at accentuating emotional features of visual-audio content. DEVA employs an Emotional Description Generator (EDG) to transmute raw audio and visual data into textualized sentiment descriptions, thereby amplifying their emotional characteristics. These descriptions are then integrated with the source data to yield richer, enhanced features. Furthermore, DEVA incorporates the Text-guided Progressive Fusion Module (TPF), leveraging varying levels of text as a core modality guide. This module progressively fuses visual-audio minor modalities to alleviate disparities between text and visual-audio modalities. Experimental results on widely used sentiment analysis benchmark datasets, including MOSI, MOSEI, and CH-SIMS, underscore significant enhancements compared to state-of-the-art models. Moreover, fine-grained emotion experiments corroborate the robust sensitivity of DEVA to subtle emotional variations.

Related papers

• SPECTRUM: Semantic Processing and Emotion-informed video-Captioning Through Retrieval and Understanding Modalities [0.7510165488300369]
  This paper proposes a novel Semantic Processing and Emotion-informed video-Captioning Through Retrieval and Understanding Modalities (SPECTRUM) framework. SPECTRUM discerns multimodal semantics and emotional themes using Visual Text Attribute Investigation (VTAI) and determines the orientation of descriptive captions. They exploit video-to-text retrieval capabilities and the multifaceted nature of video content to estimate the emotional probabilities of candidate captions.
  arXiv  Detail & Related papers  (2024-11-04T10:51:47Z)
• Generative Emotion Cause Explanation in Multimodal Conversations [23.39751445330256]
  We propose a new task, textbfMultimodal textbfConversation textbfEmotion textbfCause textbfExplanation (MCECE) It aims to generate a detailed explanation of the emotional cause to the target utterance within a multimodal conversation scenario. A novel approach, FAME-Net, is proposed, that harnesses the power of Large Language Models (LLMs) to analyze visual data and accurately interpret the emotions conveyed through facial expressions in videos.
  arXiv  Detail & Related papers  (2024-11-01T09:16:30Z)
• Facial Expression-Enhanced TTS: Combining Face Representation and Emotion Intensity for Adaptive Speech [0.13654846342364302]
  FEIM-TTS is a zero-shot text-to-speech model that synthesizes emotionally expressive speech aligned with facial images. The model is trained using LRS3, CREMA-D, and MELD datasets, demonstrating its adaptability. By integrating emotional nuances into TTS, our model enables dynamic and engaging auditory experiences for webcomics, allowing visually impaired users to enjoy these narratives more fully.
  arXiv  Detail & Related papers  (2024-09-24T16:01:12Z)
• UMETTS: A Unified Framework for Emotional Text-to-Speech Synthesis with Multimodal Prompts [64.02363948840333]
  UMETTS is a novel framework that leverages emotional cues from multiple modalities to generate highly expressive and emotionally resonant speech. EP-Align employs contrastive learning to align emotional features across text, audio, and visual modalities, ensuring a coherent fusion of multimodal information. EMI-TTS integrates the aligned emotional embeddings with state-of-the-art TTS models to synthesize speech that accurately reflects the intended emotions.
  arXiv  Detail & Related papers  (2024-04-29T03:19:39Z)
• VLLMs Provide Better Context for Emotion Understanding Through Common Sense Reasoning [66.23296689828152]
  We leverage the capabilities of Vision-and-Large-Language Models to enhance in-context emotion classification. In the first stage, we propose prompting VLLMs to generate descriptions in natural language of the subject's apparent emotion. In the second stage, the descriptions are used as contextual information and, along with the image input, are used to train a transformer-based architecture.
  arXiv  Detail & Related papers  (2024-04-10T15:09:15Z)
• Emotion Rendering for Conversational Speech Synthesis with Heterogeneous Graph-Based Context Modeling [50.99252242917458]
  Conversational Speech Synthesis (CSS) aims to accurately express an utterance with the appropriate prosody and emotional inflection within a conversational setting. To address the issue of data scarcity, we meticulously create emotional labels in terms of category and intensity. Our model outperforms the baseline models in understanding and rendering emotions.
  arXiv  Detail & Related papers  (2023-12-19T08:47:50Z)
• Exploring Emotion Expression Recognition in Older Adults Interacting with a Virtual Coach [22.00225071959289]
  EMPATHIC project aimed to design an emotionally expressive virtual coach capable of engaging healthy seniors to improve well-being and promote independent aging. This paper outlines the development of the emotion expression recognition module of the virtual coach, encompassing data collection, annotation design, and a first methodological approach.
  arXiv  Detail & Related papers  (2023-11-09T18:22:32Z)
• VISTANet: VIsual Spoken Textual Additive Net for Interpretable Multimodal Emotion Recognition [21.247650660908484]
  This paper proposes a multimodal emotion recognition system, VIsual Textual Additive Net (VISTANet) The VISTANet fuses information from image, speech, and text modalities using a hybrid of early and late fusion. The KAAP technique computes the contribution of each modality and corresponding features toward predicting a particular emotion class.
  arXiv  Detail & Related papers  (2022-08-24T11:35:51Z)
• SOLVER: Scene-Object Interrelated Visual Emotion Reasoning Network [83.27291945217424]
  We propose a novel Scene-Object interreLated Visual Emotion Reasoning network (SOLVER) to predict emotions from images. To mine the emotional relationships between distinct objects, we first build up an Emotion Graph based on semantic concepts and visual features. We also design a Scene-Object Fusion Module to integrate scenes and objects, which exploits scene features to guide the fusion process of object features with the proposed scene-based attention mechanism.
  arXiv  Detail & Related papers  (2021-10-24T02:41:41Z)
• Affective Image Content Analysis: Two Decades Review and New Perspectives [132.889649256384]
  We will comprehensively review the development of affective image content analysis (AICA) in the recent two decades. We will focus on the state-of-the-art methods with respect to three main challenges -- the affective gap, perception subjectivity, and label noise and absence. We discuss some challenges and promising research directions in the future, such as image content and context understanding, group emotion clustering, and viewer-image interaction.
  arXiv  Detail & Related papers  (2021-06-30T15:20:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.