Sentiment Analysis based Multi-person Multi-criteria Decision Making Methodology using Natural Language Processing and Deep Learning for Smarter Decision Aid. Case study of restaurant choice using TripAdvisor reviews

TL;DR

This paper introduces a novel decision-making methodology that leverages sentiment analysis of natural language reviews and deep learning to improve multi-criteria decision processes, demonstrated through a restaurant choice case study.

Contribution

The paper presents the SA-MpMcDM methodology integrating deep learning-based sentiment analysis with decision making, and introduces the TripR-2020 dataset for restaurant reviews.

Findings

Combining natural language reviews with numerical ratings enhances decision quality.

The DOC-ABSADeepL model effectively identifies aspects and opinions in reviews.

The methodology improves preference vector accuracy in decision scenarios.

Abstract

Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language. Accordingly, we propose the Sentiment Analysis based Multi-person Multi-criteria Decision Making (SA-MpMcDM) methodology for smarter decision aid, which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available. The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DOC-ABSADeepL model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria. The individual evaluations are aggregated via the procedure named criteria weighting through the attention of the experts. We evaluate the methodology in a case study of restaurant choice using TripAdvisor reviews, hence we build, manually annotate, and release the TripR-2020 dataset of restaurant reviews. We analyze the SA-MpMcDM methodology in different scenarios using and not using natural language and numerical evaluations. The analysis shows that the combination of both sources of information results in a higher quality preference vector.