Using unsupervised information to improve semi-supervised tweet sentiment classification-Reference-Cited by-同舟云学术

Using unsupervised information to improve semi-supervised tweet sentiment classification

Published:2016-08 Issue: Volume:355-356 Page:348-365
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

da Silva Nádia Félix Felipe,Coletta Luiz F.S.,Hruschka Eduardo R.,Hruschka Jr. Estevam R.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference94 articles.

1. Sentiview: Sentiment analysis and visualization for internet popular topics;Wang;IEEE Trans. Hum. Mach. Syst.,2013

2. Sentiment analysis: A review and comparative analysis of web services;Serrano-Guerrero;Inf. Sci.,2015

3. An empirical study of sentence features for subjectivity and polarity classification;Chenlo;Inf. Sci.,2014

4. Ensemble of feature sets and classification algorithms for sentiment classification;Xia;Inf. Sci.,2011

5. Using rhetorical structure in sentiment analysis;Hogenboom;Commun. ACM,2015

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