Abstract
This study proposes a novel ranking method, called SentiRank, that integrates data coming from aspect-level sentiment analysis into a multi-criteria decision aiding procedure. The novelty of the work is the combination of the evaluation of the features of a set of products (based on user preferences) with the reviews in social networks about the same products. The contribution of this work is twofold: theoretical and practical. From the theoretical side, we propose an automatic method to extract the aspect categories and their evaluations from the online opinions of users. Next, we describe how to merge that information with the decision maker’s preferences about the features of a set of products. The ELECTRE method is used afterwards to rank the products with both types of input data. From the practical side, we have implemented a tool that can be used to rank a set ofs restaurants in Tarragona, using available data and reviews on the Web. The tests show that the ranking is effectively modified when online reviews are included in the analysis.
M. Jabreel and N. Maaroof—Equal contribution.
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Acknowledgements
This work is supported by URV grant 2018PFR-URV-B2-61. N. Maaroof is funded by a URV doctoral grant 2019PMF-PIPF-17.
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Jabreel, M., Maaroof, N., Valls, A., Moreno, A. (2020). SentiRank: A System to Integrate Aspect-Based Sentiment Analysis and Multi-criteria Decision Support. In: Torra, V., Narukawa, Y., Nin, J., Agell, N. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2020. Lecture Notes in Computer Science(), vol 12256. Springer, Cham. https://doi.org/10.1007/978-3-030-57524-3_12
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