Shivam Mehta, Aarohi Mahajan, S. Chitale, Dhananjay Raut
{"title":"BirdEview:利用采矿技术的呼叫监控系统的高级版本","authors":"Shivam Mehta, Aarohi Mahajan, S. Chitale, Dhananjay Raut","doi":"10.1109/ICOEI48184.2020.9142928","DOIUrl":null,"url":null,"abstract":"The main objective is to ensure the standards of every call center for its performance, services and get insights into customer's emotions involved with the company using different data mining techniques. As the call center is the second most important thing just after the actual product it becomes very important to deal with the agent's performance and different methodologies to improve services and decrease the volume of the call flow by improving services. To achieve this, have to make sure that the agent is performing his/her task appropriately and to validate Text mining will be used to be able to monitor every call made or received by an agent. The ability to get customer's emotional insights for every issue raised by customers and escalated by the agents and these issues will be termed as Focus points. Interaction mining will be the technology used for extracting emotional insights. So firstly when agents make an outbound call to the customer or will receive an inbound call by the customer, Speaker diarisation is used to solve the problem of who spoke when during the call to perform data mining techniques live, by converting speech to text for text mining and use emotion analysis for interaction mining. After converting speech to the text, different key-points are compared and the customer's emotional input to generate a report for every call.","PeriodicalId":267795,"journal":{"name":"2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"BirdEview: Advance version of call monitoring system by using mining techniques\",\"authors\":\"Shivam Mehta, Aarohi Mahajan, S. Chitale, Dhananjay Raut\",\"doi\":\"10.1109/ICOEI48184.2020.9142928\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The main objective is to ensure the standards of every call center for its performance, services and get insights into customer's emotions involved with the company using different data mining techniques. As the call center is the second most important thing just after the actual product it becomes very important to deal with the agent's performance and different methodologies to improve services and decrease the volume of the call flow by improving services. To achieve this, have to make sure that the agent is performing his/her task appropriately and to validate Text mining will be used to be able to monitor every call made or received by an agent. The ability to get customer's emotional insights for every issue raised by customers and escalated by the agents and these issues will be termed as Focus points. Interaction mining will be the technology used for extracting emotional insights. So firstly when agents make an outbound call to the customer or will receive an inbound call by the customer, Speaker diarisation is used to solve the problem of who spoke when during the call to perform data mining techniques live, by converting speech to text for text mining and use emotion analysis for interaction mining. After converting speech to the text, different key-points are compared and the customer's emotional input to generate a report for every call.\",\"PeriodicalId\":267795,\"journal\":{\"name\":\"2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOEI48184.2020.9142928\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOEI48184.2020.9142928","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
BirdEview: Advance version of call monitoring system by using mining techniques
The main objective is to ensure the standards of every call center for its performance, services and get insights into customer's emotions involved with the company using different data mining techniques. As the call center is the second most important thing just after the actual product it becomes very important to deal with the agent's performance and different methodologies to improve services and decrease the volume of the call flow by improving services. To achieve this, have to make sure that the agent is performing his/her task appropriately and to validate Text mining will be used to be able to monitor every call made or received by an agent. The ability to get customer's emotional insights for every issue raised by customers and escalated by the agents and these issues will be termed as Focus points. Interaction mining will be the technology used for extracting emotional insights. So firstly when agents make an outbound call to the customer or will receive an inbound call by the customer, Speaker diarisation is used to solve the problem of who spoke when during the call to perform data mining techniques live, by converting speech to text for text mining and use emotion analysis for interaction mining. After converting speech to the text, different key-points are compared and the customer's emotional input to generate a report for every call.