Examining impact and benefit agreements in mineral extraction using game theory and multiple-criteria decision making

Collins, Benjamin C.; Kumral, Mustafa

doi:10.1016/j.exis.2022.101094

Cited by 6 publications

(1 citation statement)

References 43 publications

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“…where π ij is the performance of the i th alternative under the j th criterion for i = 1,2...m and j = 1, 2,...n. Madani et al [52] established the relationship between MCDM and Game theory. Various studies used this combination to solve different problems in different fields [42,[53][54][55][56]. The essential components of MCDM, viz.…”

Section: Mcdm As a Game Of Strategymentioning

confidence: 99%

Game theory and MCDM-based unsupervised sentiment analysis of restaurant reviews

Punetha

Jain

2023

Appl Intell

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Sentiment Analysis is a method to identify, extract, and quantify people's feelings, opinions, or attitudes. The wealth of online data motivates organizations to keep tabs on customers' opinions and feelings by turning to sentiment analysis tasks. Along with the sentiment analysis, the emotion analysis of written reviews is also essential to improve customer satisfaction with restaurant service. Due to the availability of massive online data, various computerized methods are proposed in the literature to decipher text sentiments. The majority of current methods rely on machine learning, which necessitates the pre-training of large datasets and incurs substantial space and time complexity. To address this issue, we propose a novel unsupervised sentiment classification model. This study presents an unsupervised mathematical optimization framework to perform sentiment and emotion analysis of reviews. The proposed model performs two tasks. First, it identifies a review's positive and negative sentiment polarities, and second, it determines customer satisfaction as either satisfactory or unsatisfactory based on a review. The framework consists of two stages. In the first stage, each review's context, rating, and emotion scores are combined to generate performance scores. In the second stage, we apply a non-cooperative game on performance scores and achieve Nash Equilibrium. The output from this step is the deduced sentiment of the review and the customer's satisfaction feedback. The experiments were performed on two restaurant review datasets and achieved state-of-the-art results. We validated and established the significance of the results through statistical analysis. The proposed model is domain and language-independent. The proposed model ensures rational and consistent results.

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