An Ensemble Model for Stance Detection in Social Media Texts

Author:

Sherif Sara S.1ORCID,Shawky Doaa M.1,Fayed Hatem A.12

Affiliation:

1. Department of Engineering Mathematics, Faculty of Engineering, Cairo University, Cairo 12613, Egypt

2. 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.

Publisher

World Scientific Pub Co Pte Ltd

Subject

General Medicine,Computer Science (miscellaneous)

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Multi-Stance Detection Method by Fusing Sentiment Features;Applied Sciences;2024-05-04

2. TwiSP: a framework for exploring polarized issues in Twitter;Proceedings of the 16th International Conference on Theory and Practice of Electronic Governance;2023-09-26

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3