{"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}
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.