文章基本信息

标题：Emotion classification of textual document using Emotion – Topic Model
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作者：S.Sujitha ; S.Selvi ; J.Martina Jasmine 等
期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2014
卷号：3
期号：2
页码：458-462
出版社：Shri Pannalal Research Institute of Technolgy
摘要：The quick development of net a pair additional and additional documents are assigned by social users with feeling labels like happiness, sadness, and surprise. Such emotions will offer a replacement facet for document categorization, and so facilitate on- line users to pick out connected documents supported their emotional preferences. The quantitative relation with manual feeling labels remains terribly small comparison to the large quantity of web/enterprise documents. The connections between social feelings and emotive terms and supported that predict the social emotion from text content mechanically. A joint feeling- topic model by augmenting Latent Dirichlet Allocation with an extra layer for emotion modeling. It generates a group of latent topics from emotions, followed by generating emotive terms from every topic. Experimental results on a web news assortment show that the projected model will effectively determine substantive latent topics for every feeling. The analysis on feeling prediction any verifies the effectiveness of the projected model.
关键词：Affective text mining; emotion-topic model