A review on machine learning techniques for text classification

As the volume of information and documents available on the internet grows, so does the demand to access it, and the need for text classification becomes evident. Due to unstructured data, most businesses today are unable to realise their full potential. It is difficult to extract valuable insights...

Full description

Saved in:
Bibliographic Details
Published in2021 4th International Conference on Computing and Communications Technologies (ICCCT) pp. 605 - 610
Main Authors Kavitha, M., Prabhavathy, P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.12.2021
Subjects
Feature engineering
Lemmatization
Machine learning
Machine learning algorithms
Scalability
Social networking (online)
Stemming
Tagging
Text categorization
Training
Online AccessGet full text
DOI10.1109/ICCCT53315.2021.9711858

Cover

More Information
Summary:As the volume of information and documents available on the internet grows, so does the demand to access it, and the need for text classification becomes evident. Due to unstructured data, most businesses today are unable to realise their full potential. It is difficult to extract valuable insights from unstructured text data from various sources such as social media, emails, conversations, webpages, surveys, and it is also hard and time-consuming process. Companies can automate business processes and make data-driven business choices in a quick and cost-effective manner using machine learning text categorization algorithms. Scalability and real-time analysis are made easier by text tagging.
DOI:10.1109/ICCCT53315.2021.9711858