{"title":"基于方面的情感分析的可移植性:30分钟的概念验证","authors":"L. Dini, Paolo Curtoni, E. Melnikova","doi":"10.1109/DSAA.2018.00085","DOIUrl":null,"url":null,"abstract":"This paper describes a system for aspect based sentiment analysis based on the assumption that domain portability should be achieved with minimal manual configuration. The approach exploits the integration of dependency parsing, graph based extraction rules over dependency trees and distributional semantics techniques. Results are considered satisfying for a \"proof of concept\" demonstrator.","PeriodicalId":208455,"journal":{"name":"2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)","volume":"4 Sect Study Dis Child 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Portability of Aspect Based Sentiment Analysis: Thirty Minutes for a Proof of Concept\",\"authors\":\"L. Dini, Paolo Curtoni, E. Melnikova\",\"doi\":\"10.1109/DSAA.2018.00085\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes a system for aspect based sentiment analysis based on the assumption that domain portability should be achieved with minimal manual configuration. The approach exploits the integration of dependency parsing, graph based extraction rules over dependency trees and distributional semantics techniques. Results are considered satisfying for a \\\"proof of concept\\\" demonstrator.\",\"PeriodicalId\":208455,\"journal\":{\"name\":\"2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)\",\"volume\":\"4 Sect Study Dis Child 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DSAA.2018.00085\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSAA.2018.00085","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Portability of Aspect Based Sentiment Analysis: Thirty Minutes for a Proof of Concept
This paper describes a system for aspect based sentiment analysis based on the assumption that domain portability should be achieved with minimal manual configuration. The approach exploits the integration of dependency parsing, graph based extraction rules over dependency trees and distributional semantics techniques. Results are considered satisfying for a "proof of concept" demonstrator.