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Machine learning techniques for automatic opinion detection in non-traditional textual genres

By Ester Boldrini, Javier Fernández Martínez, José M. Gómez and Patricio Martínez-Barco


This paper presents a preliminary study in which Machine Learning experiments applied to Opinion Mining in blogs have been carried out. We created and annotated a blog corpus in Spanish using EmotiBlog. We evaluated the utility of the features labelled firstly carrying out experiments with combinations of them and secondly using the feature selection techniques, we also deal with several problems, such as the noisy character of the input texts, the small size of the training set, the granularity of the annotation scheme and the language object of our study, Spanish, with less resource than English. We obtained promising results considering that it is a preliminary study.This paper has been supported by the next projects: “Question Answering Learning technologies in a multiLingual and Multimodal Environment (QALL-ME)” (FP6 IST-033860) and “Intelligent, Interactive and Multilingual Text Mining based on Human Language Technologies (TEXT-MESS)”(TIN2006-15265-C06-01)

Topics: Opinion mining, Sentiment analysis, Machine learning, Blogs, Emotion annotation-scheme, Feature selection, Lenguajes y Sistemas Informáticos
Publisher: WOMSA
Year: 2009
OAI identifier:

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