期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2016
卷号：7
期号：3
页码：1362-1368
出版社：TechScience Publications
摘要：Most of the algorithms for mining QuantitativeAssociation Rules (QAR) focuses on positive dependencieswithout paying particular attention to negative dependencies.The latter may be worth taking into account, however, as theyrelate the presence of certain items to the absence of others.The algorithms used to extract such rules usually consideronly one evaluation criterion in measuring the quality ofgenerated rules. Recently, some researchers have framed theprocess of extracting association rules as a multiobjectiveproblem, allowing us to jointly optimize several measures thatcan present different degrees of tradeoff depending on thedataset used. In this paper, a Multi-Objective SentimentAnalysis is done using Evolutionary Algorithm (EA) formining positive & negative Association rules. It is animportant methodological application in the world of DataMining (DM). This paper includes the approach and techniqueof Multi-Objective Positive Negative Association Rule(MOPNAR) based predictive Sentiment Analysis, which isbased on huge dataset of multiple opinion obtained. In thisstudy multiple opinions from customer, data analyst, writers,and composers has been used which are in the form of text foridentification of predictive sentiments.
