Deep Learning Model for Personality Traits Classification from Text Emphasis on Data Slicing

Abstract

Text analytics is the progression of find out insight knowledge over written text such as consumer attitude, product criticisms, emotion analysis, customer response, etc. Previous works fundamentally based on organized data, then again, writings, (for example, tweets) are inexactly organized, have poor spelling, regularly contain linguistic use errors, and they are multilingual. This made the development of much more perplexing and fascinating. The two common methodologies are used, Text-based analytics and Bag of Words. Sentiment parsing emphasis on the structure and grammar of words where Bag of words model neglects the grammar and word type and, in its place, concentrating on signifying text (a sentence, document, and tweet etc.) as the bag of words. In this research work, we have proposed a deep learning model for personality traits classification based on text. In this research paper, we emphasis on pre-processing over input data, meant for dimension reduction which may improve the classification accuracy and will reduce the computation time. In result section we will show the input data dimension reduced after pre-processing.