Sentiment and Emotion-aware Multi-criteria Fuzzy Group Decision Making System

• URL: http://arxiv.org/abs/2408.11976v1
• Date: Wed, 21 Aug 2024 20:17:06 GMT
• Title: Sentiment and Emotion-aware Multi-criteria Fuzzy Group Decision Making System
• Authors: Adilet Yerkin, Pakizar Shamoi, Elnara Kadyrgali,
• Abstract summary: Group decision-making (GDM) systems play a crucial role by facilitating consensus among participants with diverse preferences. This paper introduces a sentiment and emotion-aware multi-criteria fuzzy GDM system designed to enhance consensus-reaching effectiveness in group settings.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In today's world, making decisions as a group is common, whether choosing a restaurant or deciding on a holiday destination. Group decision-making (GDM) systems play a crucial role by facilitating consensus among participants with diverse preferences. Discussions are one of the main tools people use to make decisions. When people discuss alternatives, they use natural language to express their opinions. Traditional GDM systems generally require participants to provide explicit opinion values to the system. However, in real-life scenarios, participants often express their opinions through some text (e.g., in comments, social media, messengers, etc.). This paper introduces a sentiment and emotion-aware multi-criteria fuzzy GDM system designed to enhance consensus-reaching effectiveness in group settings. This system incorporates natural language processing to analyze sentiments and emotions expressed in textual data, enabling an understanding of participant opinions besides the explicit numerical preference inputs. Once all the experts have provided their preferences for the alternatives, the individual preferences are aggregated into a single collective preference matrix. This matrix represents the collective expert opinion regarding the other options. Then, sentiments, emotions, and preference scores are inputted into a fuzzy inference system to get the overall score. The proposed system was used for a small decision-making process - choosing the hotel for a vacation by a group of friends. Our findings demonstrate that integrating sentiment and emotion analysis into GDM systems allows everyone's feelings and opinions to be considered during discussions and significantly improves consensus among participants.

Related papers

• Towards Empathetic Conversational Recommender Systems [77.53167131692]
  We propose an empathetic conversational recommender (ECR) framework. ECR contains two main modules: emotion-aware item recommendation and emotion-aligned response generation. Our experiments on the ReDial dataset validate the efficacy of our framework in enhancing recommendation accuracy and improving user satisfaction.
  arXiv  Detail & Related papers  (2024-08-30T15:43:07Z)
• Multi-channel Emotion Analysis for Consensus Reaching in Group Movie Recommendation Systems [0.0]
  This paper proposes a novel approach to group movie suggestions by examining emotions from three different channels. We employ the Jaccard similarity index to match each participant's emotional preferences to prospective movie choices. The group's consensus level is calculated using a fuzzy inference system.
  arXiv  Detail & Related papers  (2024-04-21T21:19:31Z)
• The effect of diversity on group decision-making [11.079483551335597]
  We show that small groups can, through dialogue, overcome intuitive biases and improve individual decision-making. Across a large sample and different operationalisations, we consistently find that greater cognitive diversity is associated with more successful group deliberation.
  arXiv  Detail & Related papers  (2024-02-02T14:15:01Z)
• Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona Biases in Dialogue Systems [103.416202777731]
  We study "persona biases", which we define to be the sensitivity of dialogue models' harmful behaviors contingent upon the personas they adopt. We categorize persona biases into biases in harmful expression and harmful agreement, and establish a comprehensive evaluation framework to measure persona biases in five aspects: Offensiveness, Toxic Continuation, Regard, Stereotype Agreement, and Toxic Agreement.
  arXiv  Detail & Related papers  (2023-10-08T21:03:18Z)
• ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactions [58.71970923420007]
  We present ChoiceMates, a system that enables conversations with a dynamic set of LLM-powered agents. Agents, as opinionated personas, flexibly join the conversation, not only providing responses but also conversing among themselves to elicit each agent's preferences. Our study (n=36) comparing ChoiceMates to conventional web search and single-agent showed that ChoiceMates was more helpful in discovering, diving deeper, and managing information compared to Web with higher confidence.
  arXiv  Detail & Related papers  (2023-10-02T16:49:39Z)
• Fostering User Engagement in the Critical Reflection of Arguments [3.26297440422721]
  We propose a system that engages in a deliberative dialogue with a human. We enable the system to intervene if the user is too focused on their pre-existing opinion. We report on a user study with 58 participants to test our model and the effect of the intervention mechanism.
  arXiv  Detail & Related papers  (2023-08-17T15:48:23Z)
• Seeking Subjectivity in Visual Emotion Distribution Learning [93.96205258496697]
  Visual Emotion Analysis (VEA) aims to predict people's emotions towards different visual stimuli. Existing methods often predict visual emotion distribution in a unified network, neglecting the inherent subjectivity in its crowd voting process. We propose a novel textitSubjectivity Appraise-and-Match Network (SAMNet) to investigate the subjectivity in visual emotion distribution.
  arXiv  Detail & Related papers  (2022-07-25T02:20:03Z)
• Revealing Persona Biases in Dialogue Systems [64.96908171646808]
  We present the first large-scale study on persona biases in dialogue systems. We conduct analyses on personas of different social classes, sexual orientations, races, and genders. In our studies of the Blender and DialoGPT dialogue systems, we show that the choice of personas can affect the degree of harms in generated responses.
  arXiv  Detail & Related papers  (2021-04-18T05:44:41Z)
• DeepGroup: Representation Learning for Group Recommendation with Implicit Feedback [0.5584060970507505]
  We focus on making recommendations for a new group of users whose preferences are unknown, but we are given the decisions/choices of other groups. Given a set of groups and their observed decisions, group decision prediction intends to predict the decision of a new group of users. reverse social choice aims to infer the preferences of those users involved in observed group decisions.
  arXiv  Detail & Related papers  (2021-03-13T02:05:26Z)
• Natural Language Understanding for Argumentative Dialogue Systems in the Opinion Building Domain [6.951113351928047]
  This paper introduces a framework for argumentative dialogue systems in the information-seeking domain. Our approach distinguishes multiple user intents and identifies system arguments the user refers to in his or her natural language utterances.
  arXiv  Detail & Related papers  (2021-03-03T21:17:24Z)
• Indecision Modeling [50.00689136829134]
  It is important that AI systems act in ways which align with human values. People are often indecisive, and especially so when their decision has moral implications.
  arXiv  Detail & Related papers  (2020-12-15T18:32:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.