Users intention based on twitter features using text analytics

Qadri Mishael*, Aladdin Ayesh, Iryna Yevseyeva

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution


Online Social networks are widely used in current times. In this paper, we investigate twitter posts to identify features that feed in intention mining calculation. The posts features are divided into three sets: tweets textual features, users features, and network contextual features. In this paper, our focus is on tweets analysing textual features. As a result of this paper, we were able to create intentions profiles for 2960 users based on textual features. The prediction accuracy of three classifiers was compared for the data set, using ten intention categories to test the features. The best accuracy was achieved for SVM classifier. In the future, we plan to include user and network contextual features aiming at improving the prediction accuracy.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings
EditorsHujun Yin, Richard Allmendinger, David Camacho, Peter Tino, Antonio J. Tallón-Ballesteros, Ronaldo Menezes
Number of pages8
ISBN (Print)9783030336066
Publication statusPublished - 2019
Externally publishedYes
Event20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019 - Manchester, United Kingdom
Duration: 14 Nov 201916 Nov 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11871 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019
Country/TerritoryUnited Kingdom

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.


  • Feature selection
  • Intention mining
  • Machine learning
  • Online Social Network


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