{"title":"基于混合算法的推文情感分析","authors":"Adomar L. Ilao, Arnel C. Fajardo","doi":"10.1109/ICTKE47035.2019.8966887","DOIUrl":null,"url":null,"abstract":"Communication is a vital component of everyday life. Through technology via social media, communication becomes more dynamic generating huge volume of data. Each data represents sentiments toward a public issue. Sentiment analysis algorithms able classify whether positive, negative or neutral. This paper introduces a hybrid algorithm combining two lexicon-based algorithms namely SentiWordNet and VADER algorithms. Three algorithms were tested using different data sources. It achieved an accuracy of 88.83% which 21.44% improvement from most commonly used algorithm SentiWordNet.","PeriodicalId":442255,"journal":{"name":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Sentiment Analysis of Tweet Messages using Hybrid Approach Algorithm\",\"authors\":\"Adomar L. Ilao, Arnel C. Fajardo\",\"doi\":\"10.1109/ICTKE47035.2019.8966887\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Communication is a vital component of everyday life. Through technology via social media, communication becomes more dynamic generating huge volume of data. Each data represents sentiments toward a public issue. Sentiment analysis algorithms able classify whether positive, negative or neutral. This paper introduces a hybrid algorithm combining two lexicon-based algorithms namely SentiWordNet and VADER algorithms. Three algorithms were tested using different data sources. It achieved an accuracy of 88.83% which 21.44% improvement from most commonly used algorithm SentiWordNet.\",\"PeriodicalId\":442255,\"journal\":{\"name\":\"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTKE47035.2019.8966887\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 17th International Conference on ICT and Knowledge Engineering (ICT&KE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTKE47035.2019.8966887","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sentiment Analysis of Tweet Messages using Hybrid Approach Algorithm
Communication is a vital component of everyday life. Through technology via social media, communication becomes more dynamic generating huge volume of data. Each data represents sentiments toward a public issue. Sentiment analysis algorithms able classify whether positive, negative or neutral. This paper introduces a hybrid algorithm combining two lexicon-based algorithms namely SentiWordNet and VADER algorithms. Three algorithms were tested using different data sources. It achieved an accuracy of 88.83% which 21.44% improvement from most commonly used algorithm SentiWordNet.