Abstract
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.
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Orimaye, S.O. (2013). Learning to Classify Subjective Sentences from Multiple Domains Using Extended Subjectivity Lexicon and Subjective Predicates. In: Banchs, R.E., Silvestri, F., Liu, TY., Zhang, M., Gao, S., Lang, J. (eds) Information Retrieval Technology. AIRS 2013. Lecture Notes in Computer Science, vol 8281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45068-6_17
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DOI: https://doi.org/10.1007/978-3-642-45068-6_17
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