文章基本信息

标题：A Comparison between Sentiment Analysis of Student Feedback at Sentence Level and at Token Level
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作者：Chandrika Chatterjee ; Kunal Chakma
期刊名称：International Journal of Computer Science and Network
印刷版ISSN：2277-5420
出版年度：2015
卷号：4
期号：3
页码：482-486
出版社：IJCSN publisher
摘要：Sentiment classification is a special case of text classification whose aim is classification of a given text according to the sentimental polarities of opinions it contains, that is favorable or unfavorable, positive or negative. Student feedback is collected as response to set of positive and negative questions. The idea is to identify and extract the relevant information from feedback questions in natural language texts in order to determine a set of best predictive attributes or features for classification of unlabelled opinionated text. Sentiment classification is used for training a binary classifier using feedback questions annotated for positive or negative sentiment and evaluates the corresponding feedback received. This paper compares the sentiment classification of student feedback questions at sentence level and at token level for different classifiers
关键词：Sentiment Analysis;Tokens;Classification;Support Vector Machine;Decision Tree