Angelos Fasoulis, M. Virvou, G. Tsihrintzis, C. Patsakis, Efthymios Alepis
{"title":"Sensus Vox: Sentiment Mapping Through Smartphone Multi-Sensory Crowdsourcing","authors":"Angelos Fasoulis, M. Virvou, G. Tsihrintzis, C. Patsakis, Efthymios Alepis","doi":"10.1109/ICTAI.2018.00074","DOIUrl":null,"url":null,"abstract":"Sentiment analysis is a rather intriguing subject that modern ICT tools enable us to explore and analyze. In this work we perform, to the best of our knowledge, the most wide analysis of sentiment mapping to geographic locations and time through smartphones, in an attempt to both visualize them and also reveal possible correlations and patterns. Our vast dataset consisting of more than 56.000 samples, from 100 individuals, for a time period of nine months, revealed patterns, both in space and time, that are directly linked to geographic locations of users and provide an aggregated real-time insight on how people feel, allowing for a wide range of applications.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTAI.2018.00074","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Sentiment analysis is a rather intriguing subject that modern ICT tools enable us to explore and analyze. In this work we perform, to the best of our knowledge, the most wide analysis of sentiment mapping to geographic locations and time through smartphones, in an attempt to both visualize them and also reveal possible correlations and patterns. Our vast dataset consisting of more than 56.000 samples, from 100 individuals, for a time period of nine months, revealed patterns, both in space and time, that are directly linked to geographic locations of users and provide an aggregated real-time insight on how people feel, allowing for a wide range of applications.