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Inf Retrieval (2009) 12:526558
DOI 10.1007/s10791-008-9070-z
Erik Boiy Marie-Francine Moens
Received: 7 January 2008 / Accepted: 5 September 2008 / Published online: 26 September 2008 Springer Science+Business Media, LLC 2008
Abstract Sentiment analysis, also called opinion mining, is a form of information extraction from text of growing research and commercial interest. In this paper we present our machine learning experiments with regard to sentiment analysis in blog, review and forum texts found on the World Wide Web and written in English, Dutch and French. We train from a set of example sentences or statements that are manually annotated as positive, negative or neutral with regard to a certain entity. We are interested in the feelings that people express with regard to certain consumption products. We learn and evaluate several classication models that can be congured in a cascaded pipeline. We have to deal with several problems, being the noisy character of the input texts, the attribution of the sentiment to a particular entity and the small size of the training set. We succeed to identify positive, negative and neutral feelings to the entity under consideration with ca. 83% accuracy for English texts based on unigram features augmented with linguistic features. The accuracy results of processing the Dutch and French texts are ca. 70 and 68% respectively due to the larger variety of the linguistic expressions that more often diverge from standard language, thus demanding more training patterns. In addition, our experiments give us insights into the portability of the learned models across domains and languages. A substantial part of the article investigates the role of active learning techniques for reducing the number of examples to be manually annotated.
Keywords Opinion mining Information tracking Cross-language learning
Active learning
E. Boiy M.-F. Moens (&)
Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium e-mail: [email protected]
E. Boiye-mail: [email protected]
A machine learning approach to sentiment analysis in multilingual Web texts
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1 Introduction
Automatic sentiment analysis regards the extraction of a sentiment from an unstructured source such as text, images or audio. The recognized sentiments can be classied as positive or negative, or a more ne grained sentiment classication scheme can be used. Sentiment analysis of text, also called opinion mining, only recently...