This work is concerned with the automatic understanding of evaluative text. We
investigate sentence level opinion polarity prediction by assigning lexical polarities and
deriving sentence polarity from these with the use of contextual valence shifters. A
methodology for iterative failure analysis is developed and used to refine our lexicon and
identify new contextual shifters. Algorithms are presented that employ these new shifters
to improve sentence polarity prediction accuracy beyond that of a state-of-the-art existing
algorithm in the domain of consumer product reviews. We then apply the best
configuration of our algorithm to the domain of movie reviews. / Science, Faculty of / Computer Science, Department of / Graduate
| Identifer | oai:union.ndltd.org:UBC/oai:circle.library.ubc.ca:2429/4180 |
| Date | 11 1900 |
| Creators | Longton, Adam |
| Publisher | University of British Columbia |
| Source Sets | University of British Columbia |
| Language | English |
| Detected Language | English |
| Type | Text, Thesis/Dissertation |
| Format | 1709740 bytes, application/pdf |
| Rights | Attribution-NonCommercial-NoDerivatives 4.0 International, http://creativecommons.org/licenses/by-nc-nd/4.0/ |
Page generated in 0.002 seconds