Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model
作者机构:School of Computer Science and TechnologyBeijing Institute of TechnologyBeijing100081China The Faculty of New Information and Communication TechnologiesUniversity Abdel-Hamid Mehri Constantine 2Constantine25000Algeria Department of IT and Computer SciencePak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan
出 版 物:《Computers, Materials & Continua》 (计算机、材料和连续体(英文))
年 卷 期:2023年第76卷第9期
页 面:3345-3361页
核心收录:
学科分类:081203[工学-计算机应用技术] 08[工学] 0805[工学-材料科学与工程(可授工学、理学学位)] 0835[工学-软件工程] 0812[工学-计算机科学与技术(可授工学、理学学位)]
基 金:funded by the BeijingMunicipal Natural Science Foundation(Grant No.4212026) Foundation Enhancement Program(Grant No.2021-JCJQ-JJ-0059)
主 题:Sentiment analysis social media election prediction machine learning transformers
摘 要:Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various *** 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 ***,sentiment analysis of electionrelated tweets presents unique challenges due to the complex language used,including figurative expressions,sarcasm,and the spread of *** 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 ***-related tweets pose unique challenges for sentiment analysis due to their complex language,sarcasm,*** 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 *** 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 *** performance of differentmodels was compared using the 2020 United States(US)Presidential Election as a case *** ElecBERT-English and ElecBERT-Multi-Languages models outperformed BERTweet,with the ElecBERT-English model achieving aMean Absolute Error(MAE)of *** 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.