{"title":"基于词汇特征的opinion .id问题分类","authors":"C. Saputra, Derwin Suhartono, Rini Wongso","doi":"10.21512/COMTECH.V8I4.4026","DOIUrl":null,"url":null,"abstract":"This research aimed to categorize questions posted in Opini.id. N-gram and Bag of Concept (BOC) were used as the lexical features. Those were combined with Naive Bayes, Support Vector Machine (SVM), and J48 Tree as the classification method. The experiments were done by using data from online media portal to categorize questions posted by user. Based on the experiments, the best accuracy is 96,5%. It is obtained by using the combination of Bigram Trigram Keyword (BTK) features with J48 Tree as classifier. Meanwhile, the combination of Unigram Bigram (UB) and Unigram Bigram Keyword (UBK) with attribute selection in WEKA achieves the accuracy of 95,94% by using SVM as the classifier.","PeriodicalId":31095,"journal":{"name":"ComTech","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Question Categorization using Lexical Feature in Opini.id\",\"authors\":\"C. Saputra, Derwin Suhartono, Rini Wongso\",\"doi\":\"10.21512/COMTECH.V8I4.4026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This research aimed to categorize questions posted in Opini.id. N-gram and Bag of Concept (BOC) were used as the lexical features. Those were combined with Naive Bayes, Support Vector Machine (SVM), and J48 Tree as the classification method. The experiments were done by using data from online media portal to categorize questions posted by user. Based on the experiments, the best accuracy is 96,5%. It is obtained by using the combination of Bigram Trigram Keyword (BTK) features with J48 Tree as classifier. Meanwhile, the combination of Unigram Bigram (UB) and Unigram Bigram Keyword (UBK) with attribute selection in WEKA achieves the accuracy of 95,94% by using SVM as the classifier.\",\"PeriodicalId\":31095,\"journal\":{\"name\":\"ComTech\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ComTech\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21512/COMTECH.V8I4.4026\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ComTech","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21512/COMTECH.V8I4.4026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Question Categorization using Lexical Feature in Opini.id
This research aimed to categorize questions posted in Opini.id. N-gram and Bag of Concept (BOC) were used as the lexical features. Those were combined with Naive Bayes, Support Vector Machine (SVM), and J48 Tree as the classification method. The experiments were done by using data from online media portal to categorize questions posted by user. Based on the experiments, the best accuracy is 96,5%. It is obtained by using the combination of Bigram Trigram Keyword (BTK) features with J48 Tree as classifier. Meanwhile, the combination of Unigram Bigram (UB) and Unigram Bigram Keyword (UBK) with attribute selection in WEKA achieves the accuracy of 95,94% by using SVM as the classifier.
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