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2014 | OriginalPaper | Chapter

Automatic Term Extraction for Sentiment Classification of Dynamically Updated Text Collections into Three Classes

Author : Yuliya Rubtsova

Published in: Knowledge Engineering and the Semantic Web

Publisher: Springer International Publishing

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This paper presents an automatic term extraction approach for building a vocabulary that is constantly updated. A prepared dictionary is used for sentiment classification into three classes (positive, neutral, negative). In addition, the results of sentiment classification are described and the accuracy of methods based on various weighting schemes is compared. The paper also demonstrates the computational complexity of generating representations for N dynamic documents depending on the weighting scheme used.

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Metadata
Title
Automatic Term Extraction for Sentiment Classification of Dynamically Updated Text Collections into Three Classes
Author
Yuliya Rubtsova
Copyright Year
2014
Publisher
Springer International Publishing
Book
Knowledge Engineering and the Semantic Web

Print ISBN: 978-3-319-11715-7

Electronic ISBN: 978-3-319-11716-4

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-11716-4_12