Ontology Driven Machine Learning Models for the Classification of Social Media Data: a Systematic Literature Review

EasyChair Preprint no. 10370

Abstract

This systematic literature review aims to explore the challenges and limitations of applying ontology driven machine learning models to the classification of social media data. Social media platforms generate a vast amount of data that requires automated and reliable classification to facilitate analysis and decision-making. Ontology driven machine learning models offer a promising approach to address this need by harnessing the power of both ontologies and machine learning algorithms to improve accuracy and efficiency. However, the application of such models to social media data classification poses unique challenges due to the complex and dynamic nature of social media data. To address this research gap, a systematic literature search was conducted, and 20 studies were included in the review. The findings of this review suggest that ontology driven machine learning models offer a promising approach to address the challenges of social media data classification. However, the existing literature highlights several challenges that need to be addressed, such as ontology development, feature selection, and model validation. Overall, the review provides insights into the current state of research on ontology driven machine learning models for social media data classification, identifies research gaps, and suggests directions for future investigation.

Keyphrases: Classification, machine learning, ontology-driven, social media

@Booklet{EasyChair:10370,
  author = {Admas Abtew and Dawit Demissie and Kula Kekeba},
  title = {Ontology Driven Machine Learning Models for the Classification of Social Media Data: a Systematic Literature Review},
  howpublished = {EasyChair Preprint no. 10370},

  year = {EasyChair, 2023}}