ENHANCING SHORT TEXT ANALYTICS THROUGH MULTIVARIATE FILTER METHODS FOR EFFICIENT FEATURE SELECTION

Feature selection is an essential process in text analytics that allows for the identification of the most pertinent elements while minimising random noise in the data. Multivariate filter methods provide robust methodology for feature selection by utilising statistical measures across several variables. The present research introduces a technique for brief text analytics that employs multivariate filter methods to pick features. The enormous volume of brief text data produced in many fields presents considerable difficulties in obtaining valuable insights from this data. A novel method is presented in this paper to improve brief text analysis by efficiently choosing the most pertinent characteristics through the use of multivariate filters. Through a sequence of meticulously planned experiments, the suggested model showcases enhanced precision, comprehensibility, and computational resource utilisation. The findings underscore notable progress in predictive metrics, suggesting that this methodology can have a large influence on the domain of text analytics. In addition to advancing natural language processing, this work provides practical applications in sentiment analysis, social media monitoring, and customer feedback interpretation. Given the ongoing evolution of digital communication, this approach is well-positioned to reveal subtle insights in concise textual material, which is frequently rich in important information.