Title :
A rule-based, domain independent approach for opinion and holder identification
Author :
Sima, Ioana Maria ; Vunvulea, Mariana
Author_Institution :
Dept. of Comput. Sci., Tech. Univ. of Cluj-Napoca, Cluj-Napoca, Romania
Abstract :
Mining sentiments from text is currently an important problem in information retrieval systems. In this paper we propose a solution for extracting opinions and opinion holders from large texts. Our goal is to achieve a high level of domain independence by implementing a rule-based approach. The results of our system have proven an accuracy which is comparable to that of systems that use a supervised learning approach, which is domain dependent.
Keywords :
data mining; information retrieval; learning (artificial intelligence); text analysis; holder identification; information retrieval systems; large texts; opinion identification; rule-based domain independent approach; supervised learning approach; text mining; Computer science; Context; Educational institutions; Information retrieval; Semantics; Speech; Supervised learning; domain independence; opinion; opinion holder; opinion target; sentiment polarity;
Conference_Titel :
Intelligent Computer Communication and Processing (ICCP), 2013 IEEE International Conference on
Conference_Location :
Cluj-Napoca
Print_ISBN :
978-1-4799-1493-7
DOI :
10.1109/ICCP.2013.6646081
