Learning to classify subjective sentences from multiple domains using extended subjectivity lexicon and subjective predicates

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Conference Proceeding

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)


We investigate the performance of subjective predicates and other extended predictive features on subjectivity classification in and across different domains. Our approach constructs a semi-supervised subjective classifier based on an extended subjectivity lexicon that includes subjective annotations resulting from a manually annotated subjectivity corpus, a list of manually constructed subjectivity clues, and a set of subjective predicates learned from a large collection of likely subjective sentences. Using the extended lexicon, we extracted high precision subjective sentences from multiple domains and constructed in-domain and cross-domain subjectivity classifiers. Experimental results on multiple datasets show that the proposed technique performed comparatively better than a high precision subjectivity classification baseline and has improved cross-domain accuracy. We report 97.7% precision, 73.4% recall and 83.8% F-Measure for in-domain subjectivity classification and a accuracy level of 84.6% for cross-domain subjectivity classification. © 2013 Springer-Verlag.

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