###### 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. opinion mining; sentiment analysis; aspect-based sentiment analysis; multiple criteria decision aid +
Footnote †: journal: Applied Sciences  
**Citation**: [PERSON]; [PERSON]; [PERSON]; [PERSON] Introducing Sentiment Analysis of Textual Reviews in a Multi-Criteria Decision Aid System. _Appl. Sci._**2021**, _11_, 216. [[https://doi.org/10.3390/app11010216](https://doi.org/10.3390/app11010216)]([https://doi.org/10.3390/app11010216](https://doi.org/10.3390/app11010216))  
Received: 17 November 2020  
Accepted: 21 December 2020  
Published: 28 December 2020  
**Published: 28 December 2020**  
(e.g., the quality of the food in a restaurant), which may be evaluated in a part of a sentence (e.g., \"Although the dinner was very expensive, the food was delicious\"). In this work we analyse how to integrate the public opinions of users about a certain set of products in a standard multi-criteria decision support system. To make this integration it is necessary to identify the aspects appearing in the reviews; to obtain the polarity associated with each aspect in each review; to integrate this information as new criteria to be considered in the MCDM process; and finally, to rank the alternatives considering the user preferences, the knowledge about the values of the alternatives in different criteria and the information about the opinions of the users on the Web. SentiRank is a system that integrates the opinions of the users on a set of products as a new set of criteria to be taken into account in ELECTRE, the well-known MCDM ranking methodology. This paper focuses especially on the aspect-based sentiment analysis task, which involves two main sub-tasks: aspect detection (detect the aspects of an object mentioned in a sentence) and aspect polarity (identify if the opinion about that aspect of the object is positive or negative). To summarise, the main contributions of this work are the following:  
1. We propose a new sentiment analysis system to obtain the polarity associated with each aspect mentioned in every sentence of a review. First, the relevant aspects of a sentence are detected; after that, a Support Vector Machine (SVM) is employed to calculate the polarity of the opinion of the user for each aspect. The experiments show that this new system has a performance comparable to the one of the current state-of-the-art systems. 2. We integrate the sentiment information about the aspects as new (social) criteria to be considered, alongside the standard criteria of the domain. Then, we apply ELECTRE-III to rank the alternatives. 3. We show how the rankings change when social criteria are considered in a use case with restaurant data. The rest of the article is structured as follows. Section 2 overviews the state-of-the-art in the fields of SA (specially ABSA) and MCDA.