Comparison of SVM and Naïve Bayes Algorithms with InNER enriched to Predict Hate Speech

Q4 Engineering
Isnen HADİ AL GHOZALİ, Arif PİRMAN, Indra INDRA
{"title":"Comparison of SVM and Naïve Bayes Algorithms with InNER enriched to Predict Hate Speech","authors":"Isnen HADİ AL GHOZALİ, Arif PİRMAN, Indra INDRA","doi":"10.31202/ecjse.1325078","DOIUrl":null,"url":null,"abstract":"Hate speech is one of the negative sides of social media abuse. Hate speech can be classified into insults, defamation, unpleasant acts, provoking, inciting, and spreading fake news (hoax). The purpose of this study is to compare the SVM and Naïve Bayes methods with feature extraction in the form of Indonesian NER (InNER) for detecting hate speech. To obtain the best model, this study applies five steps: a) data collection; b) data preprocessing; c) feature engineering; d) model development; and e) evaluating and comparing models. In this study, we have collected 7100 tweets as an initial dataset. After manual annotation, this study produced 1681 tweets: 548 insult tweets, 288 blasphemy tweets, 272 provocative tweets, and 573 neutral tweets. This study use two Python libraries that accommodate NER in Indonesian, namely the NLTK library and the Polyglot library. Based on the results of the evaluation of the proposed model, model 5, which develops the SVM algorithm with the NLTK library, is the best model proposed. This model shows an accuracy score of 92.88% with a precision of 0.93, a recall of 0.93, and an F-1 score of 0.92.","PeriodicalId":52363,"journal":{"name":"El-Cezeri Journal of Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"El-Cezeri Journal of Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31202/ecjse.1325078","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0

Abstract

Hate speech is one of the negative sides of social media abuse. Hate speech can be classified into insults, defamation, unpleasant acts, provoking, inciting, and spreading fake news (hoax). The purpose of this study is to compare the SVM and Naïve Bayes methods with feature extraction in the form of Indonesian NER (InNER) for detecting hate speech. To obtain the best model, this study applies five steps: a) data collection; b) data preprocessing; c) feature engineering; d) model development; and e) evaluating and comparing models. In this study, we have collected 7100 tweets as an initial dataset. After manual annotation, this study produced 1681 tweets: 548 insult tweets, 288 blasphemy tweets, 272 provocative tweets, and 573 neutral tweets. This study use two Python libraries that accommodate NER in Indonesian, namely the NLTK library and the Polyglot library. Based on the results of the evaluation of the proposed model, model 5, which develops the SVM algorithm with the NLTK library, is the best model proposed. This model shows an accuracy score of 92.88% with a precision of 0.93, a recall of 0.93, and an F-1 score of 0.92.
SVM与Naïve内部富集贝叶斯算法预测仇恨言论的比较
仇恨言论是社交媒体滥用的负面影响之一。仇恨言论可以分为侮辱、诽谤、令人不快的行为、挑衅、煽动和传播假新闻(恶作剧)。本研究的目的是比较SVM和Naïve贝叶斯方法与印度尼西亚NER (InNER)形式的特征提取在仇恨言论检测中的应用。为了获得最佳模型,本研究采用了五个步骤:a)数据收集;B)数据预处理;C)特征工程;D)模型开发;e)评价和比较模型。在本研究中,我们收集了7100条tweet作为初始数据集。经过人工标注,本研究共产生1681条推文:侮辱推文548条,亵渎推文288条,挑衅推文272条,中性推文573条。本研究使用两个Python库来适应印尼的NER,即NLTK库和Polyglot库。根据模型的评价结果,利用NLTK库开发SVM算法的模型5是提出的最佳模型。该模型的准确率为92.88%,精密度为0.93,召回率为0.93,F-1得分为0.92。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
El-Cezeri Journal of Science and Engineering
El-Cezeri Journal of Science and Engineering Chemical Engineering-Chemical Engineering (all)
CiteScore
1.00
自引率
0.00%
发文量
49
审稿时长
5 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信