Users intention based on twitter features using text analytics

Qadri Mishael; Aladdin Ayesh; Iryna Yevseyeva

doi:10.1007/978-3-030-33607-3_14

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 proceeding › Published conference contribution

Abstract

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 language	English
Title of host publication	Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings
Editors	Hujun Yin, Richard Allmendinger, David Camacho, Peter Tino, Antonio J. Tallón-Ballesteros, Ronaldo Menezes
Publisher	Springer
Pages	121-128
Number of pages	8
ISBN (Print)	9783030336066
DOIs	https://doi.org/10.1007/978-3-030-33607-3_14
Publication status	Published - 2019
Event	20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019 - Manchester, United Kingdom Duration: 14 Nov 2019 → 16 Nov 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11871 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019
Country/Territory	United Kingdom
City	Manchester
Period	14/11/19 → 16/11/19

Keywords

Feature selection
Intention mining
Machine learning
Online Social Network

Access to Document

10.1007/978-3-030-33607-3_14

Cite this

Mishael, Q., Ayesh, A., & Yevseyeva, I. (2019). Users intention based on twitter features using text analytics. In H. Yin, R. Allmendinger, D. Camacho, P. Tino, A. J. Tallón-Ballesteros, & R. Menezes (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings (pp. 121-128). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11871 LNCS). Springer . https://doi.org/10.1007/978-3-030-33607-3_14

Users intention based on twitter features using text analytics. / Mishael, Qadri; Ayesh, Aladdin; Yevseyeva, Iryna.

Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. ed. / Hujun Yin; Richard Allmendinger; David Camacho; Peter Tino; Antonio J. Tallón-Ballesteros; Ronaldo Menezes. Springer , 2019. p. 121-128 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11871 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Mishael, Q, Ayesh, A & Yevseyeva, I 2019, Users intention based on twitter features using text analytics. in H Yin, R Allmendinger, D Camacho, P Tino, AJ Tallón-Ballesteros & R Menezes (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11871 LNCS, Springer , pp. 121-128, 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, Manchester, United Kingdom, 14/11/19. https://doi.org/10.1007/978-3-030-33607-3_14

Mishael Q, Ayesh A, Yevseyeva I. Users intention based on twitter features using text analytics. In Yin H, Allmendinger R, Camacho D, Tino P, Tallón-Ballesteros AJ, Menezes R, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. Springer . 2019. p. 121-128. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-33607-3_14

Mishael, Qadri ; Ayesh, Aladdin ; Yevseyeva, Iryna. / Users intention based on twitter features using text analytics. Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. editor / Hujun Yin ; Richard Allmendinger ; David Camacho ; Peter Tino ; Antonio J. Tallón-Ballesteros ; Ronaldo Menezes. Springer , 2019. pp. 121-128 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Users intention based on twitter features using text analytics",

abstract = "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.",

keywords = "Feature selection, Intention mining, Machine learning, Online Social Network",

author = "Qadri Mishael and Aladdin Ayesh and Iryna Yevseyeva",

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year = "2019",

doi = "10.1007/978-3-030-33607-3_14",

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AU - Yevseyeva, Iryna

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AB - 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.

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Users intention based on twitter features using text analytics

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Keywords

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