Boost Phrase-level Polarity Labelling with Review-level Sentiment ClassificationPart of this work was supported by Natural Science Foundation (60903107, 61073071) and National High Technology Research and Development (863) Program (2011AA01A205) of China, and the third author is sponsored by the National Science Foundation (IIS-0713111). The opinions, findings, suggestions or conclusions expressed in this paper are the authors’, and do not necessarily reflect those of the sponsors.This paper is an extended version of the work Do users rate or review? boost phrase-level sentiment labeling with review-level sentiment classification in SIGIR’14.
Table 3: The precisions of review-level sentiment orientation labelling using different methods.
['[EMPTY]', '1-Overall', '2-Normalize', '3-Subaspect', '4-Classify']
We use precision to evaluate the performance of each method, and the golden standard is our human annotations. where "Pos.Review" and "Neg. Review" represent the precisions of labelling positive and negative reviews, respectively, and "Overall" is the overall performance of review-level orientation labelling.