Classifying text using machine learning models and determining conversation drift

TL;DR

This paper explores text classification using machine learning models to analyze semantic content and detect conversation drift, particularly identifying when a teacher should intervene in discussions.

Contribution

It introduces the application of natural language feature extraction with basic machine learning models to identify off-topic conversations in educational settings.

Findings

Naive Bayes, Logistic Regression, and SVM effectively classify text for conversation relevance.

The models can detect conversation drift with reasonable accuracy.

The approach aids in timely teacher intervention in discussions.

Abstract

Text classification helps analyse texts for semantic meaning and relevance, by mapping the words against this hierarchy. An analysis of various types of texts is invaluable to understanding both their semantic meaning, as well as their relevance. Text classification is a method of categorising documents. It combines computer text classification and natural language processing to analyse text in aggregate. This method provides a descriptive categorization of the text, with features like content type, object field, lexical characteristics, and style traits. In this research, the authors aim to use natural language feature extraction methods in machine learning which are then used to train some of the basic machine learning models like Naive Bayes, Logistic Regression, and Support Vector Machine. These models are used to detect when a teacher must get involved in a discussion when the lines go off-topic.