The paper proposes a framework for emotional recognition and sentiment analysis utilizing AI and a combination of facial analysis and text-based emotional modelling to aid individuals in improving their emotional health. The framework uses DeepFace software to analyse other people's facial characteristics, such as emotional expression, age, gender, and the quantity of faces present, and utilizes face preprocessing techniques (i.e. , face detection, alignment, and normalization) to improve facial recognition. Textual data is analysed by various types of transformer-based learning models (DistilRoBERTa and RoBERTa, in the case of emotional and sentiment detection, respectively). Additionally, the framework incorporates a variety of fallback strategies that create outputs under limited resource conditions, through randomization of the number of faces, age, and gender, and based on the identified emotional characteristics of the referenced text data. The framework is trained and evaluated using data from the FER-2013 and AffectNet databases to be capable of recognizing multiple types of emotion rather than just using positive or negative sentiment detection methodology. User interface-related tools developed for the proposed framework will aid in the creation of emotion diaries and long-term mood assessments to enhance users' decision-making processes and provide them with customized recommendations. This framework will ultimately guide the development of an empathetic AI system to assist with managing mental wellness and develop the basis for a future, contextually aware, and holistic emotional recognition and sentiment analysis based on a combination of face-based analyses performed by DeepFace and text-based analyses performed by transformer-supported methods, as well as fallback strategies.
Affective Computing, Deep Learning, Emotion Recognition, Mental Wellness, Sentiment Analysis
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