Introducing sentiment analysis of textual reviews in a multi-criteria decision aid system

Abstract

Nowadays, most decision processes rely not only on the preferences of the decision maker but also on the public opinions about the possible alternatives. The user preferences have been heavily taken into account in the multi-criteria decision making field. On the other hand, sentiment analysis is the field of natural language processing devoted to the development of systems that are capable of analysing reviews to obtain their polarity. However, there have not been many works up to now that integrate the results of this process with the analysis of the alternatives in a decision support system. SentiRank is a novel system that takes into account both the preferences of the decision maker and the public online reviews about the alternatives to be ranked. A new mechanism to integrate both aspects into the ranking process is proposed in this paper. The sentiments of the reviews with respect to different aspects are added to the decision support system as a set of additional criteria, and the ELECTRE methodology is used to rank the alternatives. The system has been implemented and tested with a restaurant data set. The experimental results confirm the appeal of adding the sentiment information from the reviews to the ranking process.