Dr. E.Elakiya, Dr. S. Deepa Nivethika, Dr. R. Kanagaraj, Dr.R.Sujithra, Tejus Paturu, Student
{"title":"Text Feedback Classification using Machine Learning Techniques","authors":"Dr. E.Elakiya, Dr. S. Deepa Nivethika, Dr. R. Kanagaraj, Dr.R.Sujithra, Tejus Paturu, Student","doi":"10.1109/ICECAA58104.2023.10212398","DOIUrl":null,"url":null,"abstract":"The popularity of online shopping has grown worldwide, making it an integral part of many people's lives. As customers are free to express their emotions online, online sales have become a significant source of revenue. This enables obtaining honest feedback for various products, helping to understand not only what is popular but also the overall consensus. To make sense of the large amounts of product feedback and gauge the public's response, it is important to understand the widely held sentiments. Machine learning models provide a solution to extract feedback from text. Random Forest classifier produces the highest accuracy of 88 percentage.","PeriodicalId":114624,"journal":{"name":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","volume":"93 5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 2nd International Conference on Edge Computing and Applications (ICECAA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICECAA58104.2023.10212398","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The popularity of online shopping has grown worldwide, making it an integral part of many people's lives. As customers are free to express their emotions online, online sales have become a significant source of revenue. This enables obtaining honest feedback for various products, helping to understand not only what is popular but also the overall consensus. To make sense of the large amounts of product feedback and gauge the public's response, it is important to understand the widely held sentiments. Machine learning models provide a solution to extract feedback from text. Random Forest classifier produces the highest accuracy of 88 percentage.
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