Abstract

Sentiment analysis is a field that has experienced considerable growth over the last decade. This area of research attempts to determine the opinions of people on something or someone. This article introduces a novel technique for association rule extraction in text called Extended Association Rules in Semantic Vector Spaces (AR-SVS). The objective of this analysis is to explore the feasibility of applying AR-SVS in the field of opinion mining and sentiment analysis. This new method is based on the construction of association rules, which are extended through a similarity criteria for terms represented in a semantic vector space. The method was evaluated on a sentiment analysis data set consisting of scientific paper reviews. A quantitative and qualitative analysis is done with respect to the classification performance and the generated rules. The results show that the method is competitive compared to the baseline provided by Naive Bayes and Support Vector Machines. Furthermore, previous work on the evaluation of scientific paper reviews (the Scoring Algorithm) has been used in conjunction with association rules to obtain a method that shows a superior behaviour compared to the baseline. Finally, additional experiments are performed on various multidomain data sets in order to evaluate the results of AR-SVS in different settings.