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 2: The average ratings and the coefficient of variations of the overall rating and sub-aspect ratings.
['[EMPTY]', 'Overall', 'Flavour', 'Environment', 'Service']
We see that user ratings tend to center around 4 stars on overall rating, while they tend to center around 2∼3 stars on the sub-aspect ratings. This implies that the overall rating might not serve as a real reflection of users’ feelings, and users tend to "tell the truth" in much detailed sub-aspects. In order to examine the statistical significance, we calculate the average rating μ and coefficient of variation cv =σ/μ for the overall rating and three kinds of sub-aspect ratings, where σ is the standard deviation. We see that users tend to give higher scores on overall rating, and the scores on overall rating are more concentrated.