APPLICATION OF MACHINE LEARNING TO TEXT CLASSIFICATION

Abstract

The information superhighway provides important principles for giving out information to various consultations. Organizations depend on knowing customer observations about products and services. Data can be enormous to process physically. This study investigates a technique applying Python programming to collect datasets instinctively. The use of machine learning models evolves by applying Random Forest and Naïve Bayes algorithms. These techniques are applied to the data collected for text classification purposes. This process distributes data into; positive, negative, slightly negative, slightly positive, or neutral. The results from the study show the Random Forest classifier is more efficient than the Naïve Bayes algorithm, resulting in an accuracy rate of 76.5% about Naïve Bayes (70.01%). This technique enables organizations to receive insights into customer ways of thinking.