Classifying text using machine learning models and determining conversation drift

• URL: http://arxiv.org/abs/2211.08365v1
• Date: Tue, 15 Nov 2022 18:09:45 GMT
• Title: Classifying text using machine learning models and determining conversation drift
• Authors: Chaitanya Chadha, Vandit Gupta, Deepak Gupta, Ashish Khanna
• Abstract summary: 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.
• Score: 4.785406121053965
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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