Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model  

作　　者：Asif Khan Huaping Zhang Nada Boudjellal Arshad Ahmad Maqbool Khan 

机构地区：[1]School of Computer Science and Technology,Beijing Institute of Technology,Beijing,100081,China [2]The Faculty of New Information and Communication Technologies,University Abdel-Hamid Mehri Constantine 2,Constantine,25000,Algeria [3]Department of IT and Computer Science,Pak-Austria Fachhochschule:Institute of Applied Sciences and Technology,Haripur,22620,Pakistan

出　　处：《Computers, Materials & Continua》2023年第9期3345-3361,共17页计算机、材料和连续体（英文）

基　　金：funded by the BeijingMunicipal Natural Science Foundation(Grant No.4212026);Foundation Enhancement Program(Grant No.2021-JCJQ-JJ-0059).

摘　　要：Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various topics.In recent years,the rise of social media platforms(SMPs)has provided a rich source of data for analyzing public opinions,particularly in the context of election-related conversations.Nevertheless,sentiment analysis of electionrelated tweets presents unique challenges due to the complex language used,including figurative expressions,sarcasm,and the spread of misinformation.To address these challenges,this paper proposes Election-focused Bidirectional Encoder Representations from Transformers(ElecBERT),a new model for sentiment analysis in the context of election-related tweets.Election-related tweets pose unique challenges for sentiment analysis due to their complex language,sarcasm,andmisinformation.ElecBERT is based on the Bidirectional Encoder Representations from Transformers(BERT)language model and is fine-tuned on two datasets:Election-Related Sentiment-Annotated Tweets(ElecSent)-Multi-Languages,containing 5.31 million labeled tweets in multiple languages,and ElecSent-English,containing 4.75million labeled tweets in English.Themodel outperforms othermachine learning models such as Support Vector Machines(SVM),Na飗e Bayes(NB),and eXtreme Gradient Boosting(XGBoost),with an accuracy of 0.9905 and F1-score of 0.9816 on ElecSent-Multi-Languages,and an accuracy of 0.9930 and F1-score of 0.9899 on ElecSent-English.The performance of differentmodels was compared using the 2020 United States(US)Presidential Election as a case study.The ElecBERT-English and ElecBERT-Multi-Languages models outperformed BERTweet,with the ElecBERT-English model achieving aMean Absolute Error(MAE)of 6.13.This paper presents a valuable contribution to sentiment analysis in the context of election-related tweets,with potential applications in political analysis,social media management,and policymaking.

关 键 词：Sentiment analysis social media election prediction machine learning TRANSFORMERS 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]