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Identifying Psychological States of Human Subjects via Machine Learning Algorithms

This research project investigates the application of artificial intelligence and machine learning techniques to the identification of human psychological states. The study collects multimodal information from participants during structured conversational interactions and combines these data with established psychological and demographic measures. The research explores whether machine learning algorithms can identify patterns associated with psychological states using behavioural, physiological and conversational information. Participants take part in structured conversations involving both artificial intelligence and another human participant. The conversational activities include emotional reflection, cognitive challenges and open-ended discussion. During the emotional reflection activity, participants discuss experiences such as happiness, stress and coping strategies. The cognitive component includes memory tasks, riddles, planning questions and problem-solving or moral-dilemma questions. The final component involves an open conversation around topics such as travel, artificial intelligence, books or films. The ChatGPT interaction is designed to provide a consistent conversational structure while allowing participants to respond naturally. The study collects several forms of multimodal data. These include voice recordings, video recordings showing participants' facial expressions, spoken conversation transcripts and EEG data. Additional demographic and lifestyle information may also be collected. The research materials describe factors such as age, marital status, weight, height, disability, smoking, drinking, academic performance, family income, family size and physical exercise. Established questionnaires are also used to obtain information about participants' experiences and psychological states. Machine learning techniques are subsequently applied to the collected datasets to identify patterns that may be associated with psychological states. The research materials describe potential applications of AI in psychological assessment, predictive analytics, emotion recognition, monitoring and prevention, personalised treatment and AI-assisted mental health support. The broader objective is to investigate whether computational approaches can contribute to earlier identification of psychological difficulties and support more accessible and personalised approaches to mental health assessment. The project also places emphasis on participant welfare, informed consent, confidentiality and secure handling of research data. Participants are informed that they can withdraw their data up to completion of the study and are provided with support information if participation raises concerns or emotional discomfort. The consent documentation confirms that participants are informed about voice, video and photographic recording, data handling and access arrangements. The supplied materials also contain separate instructions for a conversational AI study in which participants interact with ChatGPT for at least ten minutes. The interaction is recorded and the resulting conversation transcript is retained for research purposes. The project therefore provides a multimodal research setting for examining human psychological states through conversational, behavioural and machine-learning approaches.

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Multimodal Sentiment Intelligence Platform for Dynamic Market Insights

This assignment presents the development of a Multimodal Sentiment Intelligence Platform for Dynamic Market Insights. The project addresses the need for real-time market sentiment analysis by combining text and visual data through an AI-driven multimodal approach. Traditional sentiment analysis methods may have limitations when dealing with diverse data modalities, and this project aims to address this gap by integrating multiple artificial intelligence and deep learning techniques. The primary objective is to develop an AI-driven platform capable of performing real-time sentiment analysis and supporting market trend prediction. The proposed system also aims to generate business intelligence insights that can support applications in marketing, finance, and customer service. The project incorporates deep learning, large language models, and multimodal fusion techniques to improve sentiment understanding. For text-based sentiment analysis, the assignment identifies BERT and GPT-2 for sentiment classification. For visual sentiment analysis, YOLOv8 is used for object detection while DeepFace is incorporated for facial emotion recognition. Feature-level and decision-level fusion strategies are applied to combine information from different modalities and improve the overall sentiment analysis process. Retrieval-Augmented Generation (RAG) is also incorporated to provide context-aware sentiment insights. The proposed platform uses Amazon reviews and IMDb reviews as text datasets. For image or video-based multimodal data, the assignment references the CMU-MOSEI dataset and an Amazon-Reddit merged reviews dataset. Overall, the work focuses on combining natural language processing, computer vision, deep learning, large language models, multimodal fusion, and retrieval-augmented generation to create a platform capable of producing dynamic sentiment and market insights.

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