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.
Highlights
The surge of social networks, full of opinions about all possible kinds of objects, has provoked a strong change in decision making
The combination of aspect-based sentiment analysis and intuitionistic fuzzy sets to rank a set of items was proposed in [36]; in that case, the employed MCDM mechanism was we propose to use the power of aspect-based sentiment analysis systems and ELECTRE ranking methods to develop a novel ranking system
Decision making is a very hard task, as it often requires the analysis of hundreds of potential alternatives defined on multiple and conflict criteria
Summary
The surge of social networks, full of opinions about all possible kinds of objects, has provoked a strong change in decision making. It is arguably important to integrate the information about the preferences of the user with the public opinions on the products in a decision support system. The management of preferences in ranking systems [6,7] and the automated analysis of the sentiments of texts [8] have been heavily studied in their respective fields. Sentiment polarity is the most well-studied sub-problem of sentiment analysis It may be conceptualised as a multi-class text classification problem, in which the goal is to determine whether the overall opinion expressed in a given text is positive, negative or even neutral. The intensity is a real value in the range from 0 (negative) to 1 (positive) Since it is a text classification problem, we can apply any existing supervised learning method, e.g., naive Bayes or Support Vector
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
