Show simple item record

AuthorBouras, Abdelaziz
AuthorZhang, Haiqing
AuthorSekhari, Aicha
AuthorOuzrout, Yacine
Available date2020-08-18T08:34:17Z
Publication Date2019
Publication NameProceedings of the 2015 International Conference on Artificial Intelligence, ICAI 2015 - WORLDCOMP 2015
ResourceScopus
URIhttp://hdl.handle.net/10576/15602
AbstractOpinion mining mainly involves three elements: feature and feature-of relations, opinion expressions and the related opinion attributes (e.g. Polarity), and feature-opinion relations. Although many works are emerged to achieve the aim of gaining information, the previous researches typically handle each of the three elements in isolation that cannot give the sufficient information extraction results and hence increases the complexity and the running time of information extraction. In this paper, we propose an opinion mining extraction algorithm to jointly discover the main opinion mining elements. Specifically, the algorithm automatically builds kernels to combine closest words into new terms from word level to phrase level based on dependency relations, and we ensure the accuracy of opinion expressions and polarity based on fuzzy measurements, opinion degree intensifiers, and opinion patterns. The analyzed reviews show that the proposed algorithm can effectively identify the main elements simultaneously and outperform the baseline methods. - 2019 ICAI 2015 - WORLDCOMP 2015. All rights reserved.
Languageen
PublisherCSREA Press
SubjectDependency relations
Feature-by-feature analysis
Fuzzy sets and logic
Opinion degree intensifiers
Opinion mining
TitleIdentifying opinion mining elements based on dependency relations and fuzzy logic
TypeConference Paper
Pagination168-174
dc.accessType Abstract Only


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record