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An Ensemble Model for Stance Detection in Social Media Texts

Author

Listed:
  • Sara S. Sherif

    (Department of Engineering Mathematics, Faculty of Engineering, Cairo University, Cairo 12613, Egypt)

  • Doaa M. Shawky

    (Department of Engineering Mathematics, Faculty of Engineering, Cairo University, Cairo 12613, Egypt)

  • Hatem A. Fayed

    (Department of Engineering Mathematics, Faculty of Engineering, Cairo University, Cairo 12613, Egypt†University of Science and Technology, Mathematics Program, Zewail City of Science and Technology, October Gardens, 6th of October, Giza 12578, Egypt)

Abstract

The aim of this paper is to develop a model to classify the stance expressed in social media texts. More specifically, the work presented focuses on tweets. In stance detection (SD) tasks, the objective is to identify the stance of a person towards a target of interest. In this paper, a model for SD is established and its variations are evaluated using different classifiers. The single models differ based on the pre-processing and the combination of features. To reduce the dimensionality of the feature space, analysis of variance (ANOVA) test is used. Then, two classifiers are employed as base learners including Random Forests (RF) and Support Vector Machines (SVM). Experimental analyses are conducted on SemEval dataset that is used as a benchmark for SD. Finally, the base learners that resulted from different design alternatives, are combined into three ensemble models. Experimental results show the significance of the used features and the effectiveness of a manually built dictionary that is used in the pre-processing stage. Moreover, the proposed ensembles outperform the state-of-the-art models in the overall test score, which suggests that ensemble learning is the best tool for effective SD in tweets.

Suggested Citation

  • Sara S. Sherif & Doaa M. Shawky & Hatem A. Fayed, 2023. "An Ensemble Model for Stance Detection in Social Media Texts," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 22(02), pages 737-775, March.
    • Handle: RePEc:wsi:ijitdm:v:22:y:2023:i:02:n:s0219622022500481
    DOI: 10.1142/S0219622022500481
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