Universal, Unsupervised (Rule-Based), Uncovered Sentiment Analysis\ThanksNOTICE: this is the authors version of a work that was accepted for publication in Knowledge-Based Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version has been published in Knowledge-Based systems (http://dx.doi.org/10.1016/j.knosys.2016.11.014). ©2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Table 3: Accuracy (%) on Pang and Lee 2004 test set [Pang and Lee2004].
['Rules', '[BOLD] SentiStrength', '[BOLD] SO-CAL', '[BOLD] Our system']
Our system again performs better than so-cal for negation and intensification (although it does not behave as well when dealing with irrealis, probably due to the need for more complex compositional operations to handle this phenomenon), and also better than SentiStrength on long movie reviews.