Authors:
Darius Andrei Suciu
;
Vlad Vasile Itu
;
Alexandru Cristian Cosma
;
Mihaela Dinsoreanu
and
Rodica Potolea
Affiliation:
Technical University of Cluj-Napoca, Romania
Keyword(s):
Unsupervised Learning, Opinion Mining, NLP, Domain Independent Learning, Implementation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Intelligence Applications
;
Data Mining in Electronic Commerce
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Mining Multimedia Data
;
Pre-Processing and Post-Processing for Data Mining
;
Symbolic Systems
Abstract:
Considering the wide spectrum of both practical and research applicability, opinion mining has attracted increased attention in recent years. This article focuses on breaking the domain-dependency issues which occur in supervised opinion mining by using an unsupervised approach. Our work devises a methodology by considering a set of grammar rules for identification of opinion bearing words. Moreover, we focus on tuning our method for the best tradeoff between precision-recall, computation complexity and number of seed words while not committing to a specific input data set. The method is general enough to perform well using just 2 seed words therefore we can state that it is an unsupervised strategy. Moreover, since the 2 seed words are class representatives (“good”, “bad”) we claim that the method is domain independent.