不同数据预处理技术以及ML和深度学习模型在情感分析中的效果和比较:SVM、KNN、PCA with SVM和CNN

Shoaib Hafeez, Nikhila Kathirisetty
{"title":"不同数据预处理技术以及ML和深度学习模型在情感分析中的效果和比较:SVM、KNN、PCA with SVM和CNN","authors":"Shoaib Hafeez, Nikhila Kathirisetty","doi":"10.1109/ICAITPR51569.2022.9844192","DOIUrl":null,"url":null,"abstract":"In this paper, we have discussed different data pre-processing techniques and different machine learning and deep learning models which are used for sentiment analysis. The dataset used was “Restaurant Reviews” We have compared the results of different results of SVM, KNN, PCA with SVM and CNN models. Each of the different pre-processed datasets was passed to different machine learning and deep learning models and the results were compared to find the most useful data pre-processing technique for a particular model, so we can save resources (time and money) by concentrating our resources on that particular data pre-processing technique for that model.","PeriodicalId":262409,"journal":{"name":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Effects and Comparison of different Data pre-processing techniques and ML and deep learning models for sentiment analysis: SVM, KNN, PCA with SVM and CNN\",\"authors\":\"Shoaib Hafeez, Nikhila Kathirisetty\",\"doi\":\"10.1109/ICAITPR51569.2022.9844192\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we have discussed different data pre-processing techniques and different machine learning and deep learning models which are used for sentiment analysis. The dataset used was “Restaurant Reviews” We have compared the results of different results of SVM, KNN, PCA with SVM and CNN models. Each of the different pre-processed datasets was passed to different machine learning and deep learning models and the results were compared to find the most useful data pre-processing technique for a particular model, so we can save resources (time and money) by concentrating our resources on that particular data pre-processing technique for that model.\",\"PeriodicalId\":262409,\"journal\":{\"name\":\"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAITPR51569.2022.9844192\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAITPR51569.2022.9844192","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

在本文中,我们讨论了用于情感分析的不同数据预处理技术以及不同的机器学习和深度学习模型。使用的数据集为“Restaurant Reviews”,我们将SVM、KNN、PCA与SVM和CNN模型的不同结果进行了比较。每个不同的预处理数据集被传递给不同的机器学习和深度学习模型,并将结果进行比较,以找到针对特定模型最有用的数据预处理技术,因此我们可以通过将资源集中在该模型的特定数据预处理技术上来节省资源(时间和金钱)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effects and Comparison of different Data pre-processing techniques and ML and deep learning models for sentiment analysis: SVM, KNN, PCA with SVM and CNN
In this paper, we have discussed different data pre-processing techniques and different machine learning and deep learning models which are used for sentiment analysis. The dataset used was “Restaurant Reviews” We have compared the results of different results of SVM, KNN, PCA with SVM and CNN models. Each of the different pre-processed datasets was passed to different machine learning and deep learning models and the results were compared to find the most useful data pre-processing technique for a particular model, so we can save resources (time and money) by concentrating our resources on that particular data pre-processing technique for that model.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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:604180095
Book学术官方微信