TextNetTopics-SFTS-SBTS: TextNetTopics Scoring Approaches Based Sequential Forward and Backward

Voskergian D., Bakir-Gungor B., Yousef M.

11th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2024, Gran Canaria, Spain, 15 - 17 July 2024, vol.14849 LNBI, pp.343-355 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 14849 LNBI
  • Doi Number: 10.1007/978-3-031-64636-2_26
  • City: Gran Canaria
  • Country: Spain
  • Page Numbers: pp.343-355
  • Keywords: machine learning, text classification, topic modeling, topic selection
  • Abdullah Gül University Affiliated: Yes

Abstract

TextNetTopics is a text classification-based topic modeling approach that performs topic selection rather than word selection to train a machine learning algorithm. However, one main limitation of TextNetTopics is that its scoring component (the S component) assesses each topic independently and ranks them accordingly, neglecting the potential relationship between topics. In order to address this limitation and improve the classification performance, this study introduces an enhancement to TextNetTopics. TextNetTopics-SFTS-SBTS integrates two novel scoring approaches: Sequential Forward Topic Scoring (SFTS) and Sequential Backward Topic Scoring (SBTS), which consider topic interactions by assessing sets of topics simultaneously. This integration aims to streamline the topic selection process and enhance classifier efficiency for text classification. The results obtained across three datasets offer valuable insights into the context-dependent effectiveness of the new scoring mechanisms across diverse datasets and varying numbers of topics involved in the analysis.