{"title":"SentiWordNet分数适合多领域情感分类吗?","authors":"K. Denecke","doi":"10.1109/ICDIM.2009.5356764","DOIUrl":null,"url":null,"abstract":"Motivated by the numerous applications of analysing opinions in multi-domain scenarios, this paper studies the potential of a still rarely considered approach to the problem of multi-domain sentiment analysis based on Senti-WordNet as lexical resource. SentiWordNet scores are exploited together with additional features to assign a polarity to a text using machine learning. On the other hand, a rule-based approach is studied based on sentiment scores. The introduced methods are tested on single domains of a real-world data set consisting of documents in six different domains, but also in cross-domain settings. The results show that for cross-domain sentiment analysis rule-based approaches with fix opinion lexica are unsuited. For machine-learning based sentiment classification a mixture of documents of different domains achieves good results.","PeriodicalId":300287,"journal":{"name":"2009 Fourth International Conference on Digital Information Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"95","resultStr":"{\"title\":\"Are SentiWordNet scores suited for multi-domain sentiment classification?\",\"authors\":\"K. Denecke\",\"doi\":\"10.1109/ICDIM.2009.5356764\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Motivated by the numerous applications of analysing opinions in multi-domain scenarios, this paper studies the potential of a still rarely considered approach to the problem of multi-domain sentiment analysis based on Senti-WordNet as lexical resource. SentiWordNet scores are exploited together with additional features to assign a polarity to a text using machine learning. On the other hand, a rule-based approach is studied based on sentiment scores. The introduced methods are tested on single domains of a real-world data set consisting of documents in six different domains, but also in cross-domain settings. The results show that for cross-domain sentiment analysis rule-based approaches with fix opinion lexica are unsuited. For machine-learning based sentiment classification a mixture of documents of different domains achieves good results.\",\"PeriodicalId\":300287,\"journal\":{\"name\":\"2009 Fourth International Conference on Digital Information Management\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"95\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Fourth International Conference on Digital Information Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDIM.2009.5356764\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Fourth International Conference on Digital Information Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDIM.2009.5356764","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Are SentiWordNet scores suited for multi-domain sentiment classification?
Motivated by the numerous applications of analysing opinions in multi-domain scenarios, this paper studies the potential of a still rarely considered approach to the problem of multi-domain sentiment analysis based on Senti-WordNet as lexical resource. SentiWordNet scores are exploited together with additional features to assign a polarity to a text using machine learning. On the other hand, a rule-based approach is studied based on sentiment scores. The introduced methods are tested on single domains of a real-world data set consisting of documents in six different domains, but also in cross-domain settings. The results show that for cross-domain sentiment analysis rule-based approaches with fix opinion lexica are unsuited. For machine-learning based sentiment classification a mixture of documents of different domains achieves good results.