{"title":"基于情感分析的冷启动推荐和深度神经学习(SACNN):一种新的流行病旅行推荐方法","authors":"Jasmine Samraj, N. Menaka","doi":"10.1109/ICMNWC52512.2021.9688450","DOIUrl":null,"url":null,"abstract":"Travelling is always being a usual thing where people travel for particular reasons such as business meetings, vacation, medical emergencies, and get-together parties, etc. But travelling in the covid-19 situation has been a concern, where there are lots of restrictions are allotted in various cities to control the pandemic situation. To control the pandemic among the user during travelling and to obtain easy information access ‘travel recommendation correlated with social media is used. In the proposed system the system analyzes the user’s social media accounts to gather information and updates the travel history. Hereby, when a new user surfs for any travel updates the server undergoes a validation process and suggests accordingly. For recommendation purposes, the proposed system introduces a new novel mechanism named ‘Sentimental Analysis Based Cold-start recommendation with Deep Neural Learning (SACNN)’. In this method, all the recent travel and covid-19 related details are stored and saved for user check. Further, the system for security enables a fake identification classifier to detect fake information in social media. The proposed theory will provide better accuracy rate than the existing other performances.","PeriodicalId":186283,"journal":{"name":"2021 IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC)","volume":"36 5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Sentimental Analysis Based on Cold-Start Recommendation with Deep Neural Learning (SACNN): A Novel Approach for Travel Recommendation in Pandemic\",\"authors\":\"Jasmine Samraj, N. Menaka\",\"doi\":\"10.1109/ICMNWC52512.2021.9688450\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Travelling is always being a usual thing where people travel for particular reasons such as business meetings, vacation, medical emergencies, and get-together parties, etc. But travelling in the covid-19 situation has been a concern, where there are lots of restrictions are allotted in various cities to control the pandemic situation. To control the pandemic among the user during travelling and to obtain easy information access ‘travel recommendation correlated with social media is used. In the proposed system the system analyzes the user’s social media accounts to gather information and updates the travel history. Hereby, when a new user surfs for any travel updates the server undergoes a validation process and suggests accordingly. For recommendation purposes, the proposed system introduces a new novel mechanism named ‘Sentimental Analysis Based Cold-start recommendation with Deep Neural Learning (SACNN)’. In this method, all the recent travel and covid-19 related details are stored and saved for user check. Further, the system for security enables a fake identification classifier to detect fake information in social media. The proposed theory will provide better accuracy rate than the existing other performances.\",\"PeriodicalId\":186283,\"journal\":{\"name\":\"2021 IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC)\",\"volume\":\"36 5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMNWC52512.2021.9688450\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMNWC52512.2021.9688450","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sentimental Analysis Based on Cold-Start Recommendation with Deep Neural Learning (SACNN): A Novel Approach for Travel Recommendation in Pandemic
Travelling is always being a usual thing where people travel for particular reasons such as business meetings, vacation, medical emergencies, and get-together parties, etc. But travelling in the covid-19 situation has been a concern, where there are lots of restrictions are allotted in various cities to control the pandemic situation. To control the pandemic among the user during travelling and to obtain easy information access ‘travel recommendation correlated with social media is used. In the proposed system the system analyzes the user’s social media accounts to gather information and updates the travel history. Hereby, when a new user surfs for any travel updates the server undergoes a validation process and suggests accordingly. For recommendation purposes, the proposed system introduces a new novel mechanism named ‘Sentimental Analysis Based Cold-start recommendation with Deep Neural Learning (SACNN)’. In this method, all the recent travel and covid-19 related details are stored and saved for user check. Further, the system for security enables a fake identification classifier to detect fake information in social media. The proposed theory will provide better accuracy rate than the existing other performances.