Research and Applications of Foundation Models for Data Mining and Affective Computing (RAFDA)

Scope & Topics

• Techniques for utilizing foundation models (like LLMs) for data preprocessing and feature extraction in mining large datasets.
• Foundation models for anomaly detection, clustering, and classification in data mining.
• Mining unstructured data using foundation models for improved pattern recognition and data analysis.
• Sentiment analysis using foundation models in diverse applications and industries.
• Emotion recognition and affective computing through foundation models.
• Understanding human emotions through large-scale textual data and deep learning techniques.
• Investigating affective computing methods integrating linguistic and computational approaches, aiming to understand and interpret emotions, sentiments, and affective states through machine learning and foundation models, e.g., LLMs, or other data mining techniques.
• Interpretability and explainability of foundation models in affective computing.
• Utilizing foundation models, e.g., LLMs for large-scale data analysis and pattern recognition.
• Ethical considerations in deploying foundation models for data mining and affective computing.
• Addressing biases and fairness issues in foundation model-based applications.
• Societal impact and privacy concerns related to the utilization of LLMs.
• Adaptation and fine-tuning of foundation models, e.g., LLMs for domain-specific affective data.
• Applications of foundation models, e.g., LLMs in sentiment-aware decision-making processes.
• Semantic understanding and context-based affective analysis.
• Techniques to interpret and explain the predictions of foundation models in data mining and affective computing.
• Explaining the decision-making process of foundation models for better transparency and trust.
• Encompassing language modeling, text generation, sentiment analysis, summarization, and various aspects of data mining.
• Architecture and functioning of foundation models, such as LLMs (e.g., ChatGPT).
• Integration of visual, textual, and phonetic information using foundation models for improved data understanding and analysis.
• Multimodal sentiment analysis and affective computing with fusion of diverse data sources.
• Conversational AI and text generation tasks.
• Applications of foundation models, e.g., LLMs in healthcare, finance, customer service, education, and other industry-specific domains.
• Case studies and success stories showcasing the impact of foundation models in various fields.
• Hands-on sessions for participants to engage with foundation models, e.g., LLMs, experiment with applications, and brainstorm future directions in the field.

Paper Submission

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Journal Publication