{"title":"Emotable -使用机器学习和深度学习的基于情感检测的社交媒体应用","authors":"Hinal Pujara, Priyal Babel","doi":"10.1109/IATMSI56455.2022.10119332","DOIUrl":null,"url":null,"abstract":"Social media analytics and emotion recognition have gained immense popularity in recent years. Emotion recognition is a technique for identifying and detecting human emotions while utilizing technical capabilities. This paper presents emotion detection of text posted by users on a social media application and evaluates the performances of various machine learning models, individually and combined. We applied several preprocessing techniques before training the models, which helped us understand and analyze the data. This study concluded that combining all four approaches increased accuracy compared to using each separately.","PeriodicalId":221211,"journal":{"name":"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Emotable - Emotion Detection Based Social Media Application Using Machine Learning And Deep Learning\",\"authors\":\"Hinal Pujara, Priyal Babel\",\"doi\":\"10.1109/IATMSI56455.2022.10119332\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Social media analytics and emotion recognition have gained immense popularity in recent years. Emotion recognition is a technique for identifying and detecting human emotions while utilizing technical capabilities. This paper presents emotion detection of text posted by users on a social media application and evaluates the performances of various machine learning models, individually and combined. We applied several preprocessing techniques before training the models, which helped us understand and analyze the data. This study concluded that combining all four approaches increased accuracy compared to using each separately.\",\"PeriodicalId\":221211,\"journal\":{\"name\":\"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IATMSI56455.2022.10119332\",\"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 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IATMSI56455.2022.10119332","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Emotable - Emotion Detection Based Social Media Application Using Machine Learning And Deep Learning
Social media analytics and emotion recognition have gained immense popularity in recent years. Emotion recognition is a technique for identifying and detecting human emotions while utilizing technical capabilities. This paper presents emotion detection of text posted by users on a social media application and evaluates the performances of various machine learning models, individually and combined. We applied several preprocessing techniques before training the models, which helped us understand and analyze the data. This study concluded that combining all four approaches increased accuracy compared to using each separately.