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Database Performance Tuning and Query Optimization Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Identification of Sentiment Labels Based on Self-training

Authors : Zhaowei Qu, Chunye Wu, Xiaoru Wang, Yanjiao Zhao

Published in: Data Mining and Big Data

Publisher: Springer International Publishing

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Abstract

Traditional methods for sentiment classification based on supervised learning require a large amount of labeled data for training. However, It is hard to obtain enough labeled data because it can be too expensive compared with unlabeled data. In this paper, we propose an identification of sentiment labels based on self-training (ISLS) method that can make full use of the large number of labeled data. We extract sentiment expressions based on sentiment seeds by self-training, learn sentiment words on unlabeled data and annotate unlabeled data. The sentiment expressions include processing and extracting for the negative meaning of the text. The ISLS method avoids the subjective problems of manual annotation. Experiments validate the effectiveness of the proposed ISLS method.

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previous chapter A Generic Model Based on Multiple Domains for Sentiment Classification
next chapter A Self-training Method for Detection of Phishing Websites
Footnotes
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Metadata
Title
Identification of Sentiment Labels Based on Self-training
Authors
Zhaowei Qu
Chunye Wu
Xiaoru Wang
Yanjiao Zhao
Copyright Year
2018
Book
Data Mining and Big Data

Print ISBN: 978-3-319-93802-8

Electronic ISBN: 978-3-319-93803-5

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-93803-5_38

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