Personality Prediction on Social Media Platform Using Sea Lion Updated Shark Smell Optimization Assisted Hybrid Deep Learning Model

Personality is becoming a famous topic in natural language processing (NLP), as it is the most straightforward way to translate emotions and internal thoughts into a form that others can recognize. More attention has been paid recently to cognitive-based sentiment analysis based on online social media language, with an emphasis on automatically detecting user behavior, including personality traits. However, the extended training periods linked to sequential inputs and the current deep learning approaches’ limited capacity to capture words’ real (semantic) meaning provide a problem that could compromise prediction accuracy. A novel social media personality prediction model is presented in this research to address these issues by incorporating preprocessing, extraction of features, and prediction models. The model is based on textual data from Twitter. Initially, preprocessing the input text allows for more significant feature extraction and reduces input complexity. The features are then extracted from the texts that have already been preprocessed using the polarity score and semantic similarity estimate. These methods can provide brief and insightful representations that increase the accuracy of personality prediction tests. In order to predict personality traits, the derived features are then learned using a hybrid technique that combines long short-term memory (LSTM) and recurrent neural network (RNN). To make the prediction more accurate, the improved LSTM weight is optimally tuned using a new Sea Lion Updated Shark Smell Optimization (SUSSO) algorithm that combines Sea Lion Optimization (SLnO) and Shark Smell Optimization (SSO) methods. This parametric adjusting guarantees that the suggested method of personality trait prediction works as intended. Eventually, the presented model efficacy is assessed over existing models like various performance metrics. The proposed hybrid classifier [Formula: see text] SUSSO model achieves an accuracy of 0.92633 and 0.9344 for Datasets 1 and 2, while the conventional models acquired minimal ratings. Thus, the proposed model offers promising results and paves the way for more personalized and psychologically informed interactions on social media platforms.