{"title":"情感分析中识别机器学习和特征提取方法的文献分析","authors":"Markus Haberzettl, B. Markscheffel","doi":"10.1109/ICDIM.2018.8846980","DOIUrl":null,"url":null,"abstract":"The increase in daily emails sent to the customer service of companies is creating new challenges. Sentiment analysis, i.e. the automated recognition of mood and polarity in texts, is a solution to this problem, but the sentiment analysis of German emails is still an open research problem. With the help of a literature analysis we identify and analyze the most relevant machine learning methods and the corresponding feature extraction methods.","PeriodicalId":120884,"journal":{"name":"2018 Thirteenth International Conference on Digital Information Management (ICDIM)","volume":"166 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A Literature Analysis for the Identification of Machine Learning and Feature Extraction Methods for Sentiment Analysis\",\"authors\":\"Markus Haberzettl, B. Markscheffel\",\"doi\":\"10.1109/ICDIM.2018.8846980\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The increase in daily emails sent to the customer service of companies is creating new challenges. Sentiment analysis, i.e. the automated recognition of mood and polarity in texts, is a solution to this problem, but the sentiment analysis of German emails is still an open research problem. With the help of a literature analysis we identify and analyze the most relevant machine learning methods and the corresponding feature extraction methods.\",\"PeriodicalId\":120884,\"journal\":{\"name\":\"2018 Thirteenth International Conference on Digital Information Management (ICDIM)\",\"volume\":\"166 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Thirteenth International Conference on Digital Information Management (ICDIM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDIM.2018.8846980\",\"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 Thirteenth International Conference on Digital Information Management (ICDIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDIM.2018.8846980","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Literature Analysis for the Identification of Machine Learning and Feature Extraction Methods for Sentiment Analysis
The increase in daily emails sent to the customer service of companies is creating new challenges. Sentiment analysis, i.e. the automated recognition of mood and polarity in texts, is a solution to this problem, but the sentiment analysis of German emails is still an open research problem. With the help of a literature analysis we identify and analyze the most relevant machine learning methods and the corresponding feature extraction methods.