A multimodal sentiment analysis system for recognizing person aggressiveness in pain based on textual and visual information

Ghosh, Anay; Dhara, Bibhas Chandra; Pero, Chiara; Umer, Saiyed

doi:10.1007/s12652-023-04567-z

Cited by 9 publications

(2 citation statements)

References 60 publications

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“…The comprehensive surveys navigate the evolution of SA from text-based models to the realm of multimodality, exploring diverse approaches, applications, challenges, and the transformative impact of deep neural architectures, and reflecting the dynamic shift in SA trends [26,27]. Presenting an innovative multimodal SA system, the study [28] meticulously integrates text-and image-based components, utilizing deep learning for feature learning and classification. In the literature [29], researchers introduced a novel approach, utilizing a cross-modal modeling multitensor fusion network for effective emotion fusion in multimodal SA.…”

Section: Research On Multimodal Sentiment Analysismentioning

confidence: 99%

Design and Efficacy of a Data Lake Architecture for Multimodal Emotion Feature Extraction in Social Media

Fan,

2024

IET Software

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In the rapidly evolving landscape of social media, the demand for precise sentiment analysis (SA) on multimodal data has become increasingly pivotal. This paper introduces a sophisticated data lake architecture tailored for efficient multimodal emotion feature extraction, addressing the challenges posed by diverse data types. The proposed framework encompasses a robust storage solution and an innovative SA model, multilevel spatial attention fusion (MLSAF), adept at handling text and visual data concurrently. The data lake architecture comprises five layers, facilitating real-time and offline data collection, storage, processing, standardized interface services, and data mining analysis. The MLSAF model, integrated into the data lake architecture, utilizes a novel approach to SA. It employs a text-guided spatial attention mechanism, fusing textual and visual features to discern subtle emotional interplays. The model’s end-to-end learning approach and attention modules contribute to its efficacy in capturing nuanced sentiment expressions. Empirical evaluations on established multimodal sentiment datasets, MVSA-Single and MVSA-Multi, validate the proposed methodology’s effectiveness. Comparative analyses with state-of-the-art models showcase the superior performance of our approach, with an accuracy improvement of 6% on MVSA-Single and 1.6% on MVSA-Multi. This research significantly contributes to optimizing SA in social media data by offering a versatile and potent framework for data management and analysis. The integration of MLSAF with a scalable data lake architecture presents a strategic innovation poised to navigate the evolving complexities of social media data analytics.

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Section: Research On Multimodal Sentiment Analysismentioning

confidence: 99%

Design and Efficacy of a Data Lake Architecture for Multimodal Emotion Feature Extraction in Social Media

Fan,

2024

IET Software

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“…In the field of medicine, Ghosh Anay et al conducted a series of experiments on the aggressiveness of people in the event of pain [17]. Based on human tissue injuries, this experiment implements the task of classifying text emotions.…”

Section: Assisting Individuals With Mental Illnessmentioning

confidence: 99%

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

Feng

2024

ACE

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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.

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Multimodal Sentiment Analysis and Multimodal Emotion Analysis: A Review

Sharma,

Gupta

2024

Proceedings of Third International Conference on Computing and Communication Networks

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A multimodal sentiment analysis system for recognizing person aggressiveness in pain based on textual and visual information

Cited by 9 publications

References 60 publications

Design and Efficacy of a Data Lake Architecture for Multimodal Emotion Feature Extraction in Social Media

Design and Efficacy of a Data Lake Architecture for Multimodal Emotion Feature Extraction in Social Media

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

Multimodal Sentiment Analysis and Multimodal Emotion Analysis: A Review

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