Analysing Psychological Sentiment Prediction Across Modalities: Harnessing Emotion Datasets within Natural Language Processing (NLP)

Studying human emotions and feelings is a crucial element in the field of psychology, having significant implications such as evaluating mental health and improving human-computer interactions. Recently, there has been a rise in interest in examining how psychological sentiment can be predicted through various mediums, including text, audio, video, and physiological signals. By utilizing advancements in Natural Language Processing (NLP) and analysing multimodal data, this research delves into incorporating emotion datasets into NLP frameworks to improve psychological sentiment prediction. This presents present an applicability of some of the NLP techniques to predicts the Psychological Sentiment such as Deep Generating adversarial networks (D-GANs), Long short-term memory (LSTM) and gated recurrent unit (GRU). The sentimental analysis performed by the considered algorithms are implemented in python and the parameters which are considered for the results evaluation are model loss, confusion matrix, accuracy, precision, recall and f1-score. Our goal with this analysis is to offer a deeper understanding of the existing methodologies and future scope of growth in psychological sentiment prediction of research in the field of NLP.