Semi-supervised learning with lexical knowledge for opinion mining

Sentiment prediction for text has been an intriguing subject for the last few years. The goal of it is to automatically indicate the positive or negative attitude towards a topic of interest. The proliferation of user generated content on the World Wide Web has made it possible to perform large scal...

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Veröffentlicht in:2010 IEEE International Conference on Intelligent Computer Communication and Processing S. 19 - 25
Hauptverfasser: Nóra, Balla-Müller, Lemnaru, C, Potolea, R
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.08.2010
Schlagworte:
Accuracy
Classification algorithms
conjugate gradient
Data mining
Dictionaries
domain adaptation
graph regularization
lexicon
Matrix converters
Motion pictures
semi-supervised learning
sentiment analysis
Sparse matrices
ISBN:9781424482283, 1424482283
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Beschreibung
Zusammenfassung:Sentiment prediction for text has been an intriguing subject for the last few years. The goal of it is to automatically indicate the positive or negative attitude towards a topic of interest. The proliferation of user generated content on the World Wide Web has made it possible to perform large scale mining of public opinion. This paper presents an original implementation of a system that integrates a recently proposed semi-supervised learning algorithm for text polarity classification. Lexical prior knowledge is harnessed in conjunction with labeled and unlabeled documents. The presented method is based on joint sentiment analysis of documents and words and uses a bipartite graph representation of the data. Our system is integrated into Rapid Miner, which does not come yet with semi-supervised learners.
ISBN:9781424482283
1424482283
DOI:10.1109/ICCP.2010.5606469