Optimization of Language Models by Word Computing

Abstract

Word computation is a type of sentiment analysis that requires the identification not only of linguistic features, but also of the correlations of these features. There has been a great deal of research in this area. In order to understand linguistic representations and their applications in various domains of analysis, various factors such as demographics, emotions, and gender are taken into account in a transactional context. In this paper, we focus on those factors that can be extracted from existing data using natural language processing. We find that the most successful personality trait prediction models rely heavily on NLP techniques. To automate this process, researchers around the world have used a variety of machine learning and deep learning techniques. Different combinations of factors have led to different research results. We have conducted a comparative analysis of these experiments in the hope of determining the future course of action.