Paper Info
Title
Combining supervised and unsupervised polarity classification for non-english reviews
Abstract
Two main approaches are used in order to detect the sentiment polarity from reviews. The supervised methods apply machine learning algorithms when training data are provided and the unsupervised methods are usually applied when linguistic resources are available and training data are not provided. Each one of them has its own advantages and disadvantages and for this reason we propose the use of meta-classifiers that combine both of them in order to classify the polarity of reviews. Firstly, the non-English corpus is translated to English with the aim of taking advantage of English linguistic resources. Then, it is generated two machine learning models over the two corpora (original and translated), and an unsupervised technique is only applied to the translated version. Finally, the three models are combined with a voting algorithm. Several experiments have been carried out using Spanish and Arabic corpora showing that the proposed combination approach achieves better results than those obtained by using the methods separately.
Year
DOI
Venue
2013
10.1007/978-3-642-37256-8_6
CICLing (2)
Keywords
Field
DocType
non-english corpus,main approach,training data,non-english review,english linguistic resource,sentiment polarity,unsupervised polarity classification,better result,unsupervised method,arabic corpus,unsupervised technique,linguistic resource
Training set,Voting algorithm,Arabic,Pattern recognition,Sentiment analysis,Computer science,Support vector machine,Machine translation,Unsupervised learning,Natural language processing,Artificial intelligence,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
17
Authors
4
Name
Order
Citations
PageRank
José M. Perea-Ortega124325.74
Eugenio Martínez-Cámara224118.97
MaríA-Teresa MartíN-Valdivia3371.63
L. Alfonso Ureña-López420814.19