ML-NLPEmot: Machine Learning-Natural Language Processing Event-Based Emotion Detection Proactive Framework Addressing Mental Health

Ismail, Leila; Shahin, Nada; Materwala, Huned; Hennebelle, Alain; Frermann, Lea

doi:10.1109/access.2023.3343121

Cited by 3 publications

(1 citation statement)

References 60 publications

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“…Ma et al [8] and Fei et al [9] explored social graph neural network-based interactive recommendation schemes and real-time detection of events from Twitter, respectively, showcasing the application of neural networks in capturing complex social interactions. Ismail et al [10] and Li et al [11] focused on event-based emotion detection frameworks addressing mental health and semi-supervised variational user identity linkage, respectively. These studies highlight the diverse applications of machine learning in understanding social media contents.…”

Section: Related Studymentioning

confidence: 99%

MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

Arya,

Pandey,

Gopal Krishna Patro

et al. 2024

IEEE Access

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In an era where social media platforms burgeon with diverse content, effective content moderation becomes imperative to filter harmful materials. Traditional methods often grapple with the dual challenges of accuracy and computational efficiency levels. These conventional approaches typically rely on either text-based or image-based analysis, neglecting the complex interplay of multimodal content prevalent in social media scenarios. This limitation leads to suboptimal content filtering, often missing contextually nuanced or visually deceptive harmful content sets. Addressing these challenges, the proposed work introduces an innovative, hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Transformers for nuanced visual content extraction from social media posts. CNNs adeptly handle image features, while Transformers enrich the analysis with contextual textual understanding, thereby enhancing accuracy in identifying harmful contents. Additionally, a Bi-directional Attention Mechanism (BAM) is employed for efficient text-visual fusion, dynamically prioritizing relevant information and reducing computational loads. The fusion process is further refined using Genetic Algorithms (GAs) for hyperparameter optimization, streamlining the model's performance levels. The model's robustness is demonstrated through its ability to discern complex intra and inter-modal relationships within social media content, facilitated by Graph Neural Networks (GNNs). Moreover, the model's scalability and deployment efficiency on social media platforms are ensured through quantization and pruning techniques, balancing accuracy with computational practicality levels. Empirical testing on datasets from Google, Facebook, and Kaggle reveals the model's superior performance over existing methods, with notable improvements in precision, accuracy, recall, AUC, specificity, and response delay in detecting harmful contents. The model also excels in preempting potential harmful content posters, offering enhanced pre-emption metrics.

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Section: Related Studymentioning

confidence: 99%

MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

Arya,

Pandey,

Gopal Krishna Patro

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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Voice Assisted Facial Emotion Recognition System For Blind Peoples With Tensorflow Model

2024

2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

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SentiSync: A Robust System for Sentiment Detection and Analyzing the Mental Health Care with ML-Driven Algorithms

Sinchana B U,

Thanushree G Hiremath,

Priyanka H V

2024

IJARSCT

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The integration of artificial intelligence (AI) in mental health care represents a paradigm shift in the management and understanding of mental health disorders. This abstract explores the multifaceted applications of AI in mental health, emphasizing its potential to revolutionize diagnosis, treatment, and overall patient care. AI technologies, such as machine learning algorithms and natural language processing, exhibit remarkable capabilities in analysing vast datasets, identifying patterns, and extracting meaningful insights from diverse sources. In mental health, these technologies play a important role in early detection and accurate diagnosis of psychiatric conditions. By analysing a myriad of behavioural, biological, and contextual factors, AI models can provide more precise and personalized diagnostic assessments, reducing the reliance on subjective evaluations. Furthermore, AI-driven interventions are reshaping treatment approaches in mental health care. Virtual mental health assistants, powered by AI, offer scalable and accessible support, providing timely interventions and monitoring patients' well-being. Chatbots and virtual therapists equipped with sentiment analysis can involve users in natural conversations, offering empathy and support while continuously learning and adapting to individual needs

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ML-NLPEmot: Machine Learning-Natural Language Processing Event-Based Emotion Detection Proactive Framework Addressing Mental Health

Cited by 3 publications

References 60 publications

MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

MSCMGTB: A Novel Approach for Multimodal Social Media Content Moderation Using Hybrid Graph Theory and Bio-Inspired Optimization

Voice Assisted Facial Emotion Recognition System For Blind Peoples With Tensorflow Model

SentiSync: A Robust System for Sentiment Detection and Analyzing the Mental Health Care with ML-Driven Algorithms

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