集成特征和实例选择技术的意见挖掘

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Zi-Hung You, Ya-Han Hu, Chih-Fong Tsai, Yen-Ming Kuo
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引用次数: 4

摘要

观点挖掘侧重于从文本中提取极性信息。对于text_term表示,differentfeatureselectionmethods,e.g.termfrequency(TF)ortermfrequency - inverse_document_frequency_ (TF - idf), can_yield_diverse_numbers_ of text_features。> > text>分类,however,aselectedtrainingsetmaycontainnoisydocuments(oroutliers),which candegrade theclassificationperformance。Tosolve thisproblem, instanceselectioncanbe adoptedtofilteroutunrepresentativetrainingdocuments。Therefore,thisarticleinvestigatesthe opinionminingperformanceassociatedwithfeatureandinstanceselectionstepssimultaneously。两个组合过程基于在differentorders,werecompared中执行featureselection_和instanceselection_。我们使用了Specifically、twofeatureselectionmethods、namelyTFand TF-IDF和两个实例选择方法(drop3和IB3)进行比较。实验结果是通过使用三个twitter数据集来发展情绪classifiersshowedthatTF-IDFfollowedbyDROP3performsthebest。关键词特征选择,实例选择,意见挖掘,文本分类
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating Feature and Instance Selection Techniques in Opinion Mining
Opinion mining focuses on extracting polarity information from texts. For textual term representation,differentfeatureselectionmethods,e.g.termfrequency(TF)ortermfrequency– inverse document frequency (TF–IDF), can yield diverse numbers of text features. In text classification,however,aselectedtrainingsetmaycontainnoisydocuments(oroutliers),which candegrade theclassificationperformance.Tosolve thisproblem, instanceselectioncanbe adoptedtofilteroutunrepresentativetrainingdocuments.Therefore,thisarticleinvestigatesthe opinionminingperformanceassociatedwithfeatureandinstanceselectionstepssimultaneously. Two combination processes based on performing feature selection and instance selection in differentorders,werecompared.Specifically, twofeatureselectionmethods,namelyTFand TF–IDF, and two instance selection methods, namely DROP3 and IB3, were employed for comparison. The experimental results by using three Twitter datasets to develop sentiment classifiersshowedthatTF–IDFfollowedbyDROP3performsthebest. KeyWORDS Feature Selection, Instance Selection, Opinion Mining, Text Classification
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来源期刊
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
0.00%
发文量
20
审稿时长
>12 weeks
期刊介绍: The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving
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