Sentence-Level Opinion Analysis for Chinese News Documents Based on Sentiment Information of Social Tags
Jen-Yuan Yeh, Shihn-Yuarn Chen
Journal of Digital Information Management, Vol. 14, No. 1, 2016.
DOI: 
Link: http://dline.info/fpaper/jdim/v14i1/v14i1_8.pdf
Abstract:
Social tags have been considered to indirectly
reflect authorized opinions of taggers. This paper
proposes an unsupervised method which derives implicit
sentiment information from social tags to decide,
in one document, which sentences are opinionated, as
well as to annotate them with proper polarity labels.
First, for a social tag, its opinion degree is measured
by aggregating the opinion degree of related sentiment
words, in proportion to the co-occurrence relations between
sentiment words and the tag. Second, the opinion
degree of a sentence is determined by a combination
function of the opinion degree of the tags, in proportion
to the similarity between the sentence and each
tag. Finally, sentences are sorted in order of their opinion
degree, followed by a partition of the ranked list to
distinguish sentences into positively opinionated, negatively
opinionated, neutral, and non-opinionated ones.
The proposed method is examined using the Chinese
dataset of the NTCIR Opinion Analysis Task Test Collection
and found to perform well. Experimental results
testify that social tags are positively conducive to opinion
analysis.