Fusion Approach to Finding Opinions in Blogosphere
Tuesday, March 27, 2007
Kiduk Yang, Ning Yu, Alejandro Valerio, Hui Zhang and Weimao KeIn this paper, we describe a fusion approach to finding opinion about a given target in blog postings. We tackled the opinion blog retrieval task by breaking it down to two sequential subtasks: ontopic retrieval followed by opinion classification. Our opinion retrieval approach was to first apply traditional IR methods to retrieve on-topic blogs, and then boost the ranks of opinionated blogs using combined opinion scores generated by four opinion assessment methods. Our opinion module consists of Opinion Term Module, which identify opinions based on the frequency of opinion terms (i.e., terms that only occur frequently in opinion blogs), Rare Term Module, which uses uncommon/rare terms (e.g., "sooo good") for opinion classification, IU Module, which uses IU (I and you) collocations, and Adjective-Verb Module, which uses computational linguistics’ distribution similarity approach to learn the subjective language from training data.
Full Paper
posted by ICWSM at 8:37 AM
3 Comments:
This paper was nominated for the ICWSM best paper award.
It is weird that the paper does not cite the TREC 2006 Blog track overview paper, even if some provided information like the number of pooled runs and depth of pooling could only have been taken from that overview paper.
The paper should have also cited the Blog track overview paper, when describing the TREC blog track opinion retrieval task, as is tradition in TREC (also when providing the table of top 5 runs, or blog06 collection statistics, again taken unashamedly from the overview paper but without reference).
It is our bad for forgetting to put the Blog track guideline as well as overview paper on the reference list. We didn't do that on purpose and we now really feel guilty and 'shame' about it.:(
Thanks for the heads-up and we appreciate it. Will update the reference and add a note to the paper.
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