{"title":"基于情感估计难度的日语句子分类方法","authors":"Sanae Yamashita, Y. Kami, Noriyuki Okumura","doi":"10.5220/0008366303830390","DOIUrl":null,"url":null,"abstract":"The existing systems to estimate emotions extract some emotions from the given sentences in any and all circumstances. However, there are many sentences whoever cannot estimate emotional features. It follows that the systems should not extract some emotions all the time. Systems should return \"It is difficult to estimate\" as we humans do so. This paper proposes a method to classify Japanese sentences based on the difficulty level of emotion estimation. Proposed system judges the difficulty level to estimate emotions using three conditions (negative expressions, emotive expression, and machine-learned classifications). As a result, proposed system achieved 0.8 of F1 score based on mechanical evaluation.","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Classification Method for Japanese Sentences based on the Difficulty Level of Emotion Estimation\",\"authors\":\"Sanae Yamashita, Y. Kami, Noriyuki Okumura\",\"doi\":\"10.5220/0008366303830390\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The existing systems to estimate emotions extract some emotions from the given sentences in any and all circumstances. However, there are many sentences whoever cannot estimate emotional features. It follows that the systems should not extract some emotions all the time. Systems should return \\\"It is difficult to estimate\\\" as we humans do so. This paper proposes a method to classify Japanese sentences based on the difficulty level of emotion estimation. Proposed system judges the difficulty level to estimate emotions using three conditions (negative expressions, emotive expression, and machine-learned classifications). As a result, proposed system achieved 0.8 of F1 score based on mechanical evaluation.\",\"PeriodicalId\":133533,\"journal\":{\"name\":\"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5220/0008366303830390\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0008366303830390","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Classification Method for Japanese Sentences based on the Difficulty Level of Emotion Estimation
The existing systems to estimate emotions extract some emotions from the given sentences in any and all circumstances. However, there are many sentences whoever cannot estimate emotional features. It follows that the systems should not extract some emotions all the time. Systems should return "It is difficult to estimate" as we humans do so. This paper proposes a method to classify Japanese sentences based on the difficulty level of emotion estimation. Proposed system judges the difficulty level to estimate emotions using three conditions (negative expressions, emotive expression, and machine-learned classifications). As a result, proposed system achieved 0.8 of F1 score based on mechanical evaluation.