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Detection of Stress and Relaxation Magnitudes for Tweets

Published: 23 April 2018 Publication History

Abstract

The ability to automatically detect human stress and relaxation is crucial for timely diagnosing stress-related diseases, ensuring customer satisfaction in services and managing human-centric applications such as traffic management. Traditional methods employ stress-measuring scales or physiological monitoring which may be intrusive and inconvenient. Instead, the ubiquitous nature of the social media can be leveraged to identify stress and relaxation, since many people habitually share their recent life experiences through social networking sites. This paper introduces an improved method to detect expressions of stress and relaxation in social media content. It uses word sense disambiguation by word sense vectors to improve the performance of the first and only lexicon-based stress/relaxation detection algorithm TensiStrength. Experimental results show that incorporating word sense disambiguation substantially improves the performance of the original TensiStrength. It performs better than state-of-the-art machine learning methods too in terms of Pearson correlation and percentage of exact matches. We also propose a novel framework for identifying the causal agents of stress and relaxation in tweets as future work.

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  • (2023)Contrastive Learning of Stress-specific Word Embedding for Social Media based Stress DetectionProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599795(5137-5149)Online publication date: 6-Aug-2023
  • (2023)Continuous Stress Detection Based on Social MediaIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.328333827:9(4500-4511)Online publication date: Sep-2023
  • (2023)Measuring The Performance Ability Threshold Of An Individual Under Perceived Stress With Cognitive Load: A Comprehensive Approach2023 3rd International Conference on Innovative Sustainable Computational Technologies (CISCT)10.1109/CISCT57197.2023.10351267(1-6)Online publication date: 8-Sep-2023
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cover image ACM Other conferences
WWW '18: Companion Proceedings of the The Web Conference 2018
April 2018
2023 pages
ISBN:9781450356404
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • IW3C2: International World Wide Web Conference Committee

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 23 April 2018

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Author Tags

  1. sentiment analysis
  2. social media
  3. stress
  4. twitter
  5. word sense disambiguation
  6. word vectors

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  • Research-article

Funding Sources

  • European Union's Horizon 2020 research and innovation programme

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WWW '18
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  • IW3C2
WWW '18: The Web Conference 2018
April 23 - 27, 2018
Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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  • (2023)Contrastive Learning of Stress-specific Word Embedding for Social Media based Stress DetectionProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599795(5137-5149)Online publication date: 6-Aug-2023
  • (2023)Continuous Stress Detection Based on Social MediaIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.328333827:9(4500-4511)Online publication date: Sep-2023
  • (2023)Measuring The Performance Ability Threshold Of An Individual Under Perceived Stress With Cognitive Load: A Comprehensive Approach2023 3rd International Conference on Innovative Sustainable Computational Technologies (CISCT)10.1109/CISCT57197.2023.10351267(1-6)Online publication date: 8-Sep-2023
  • (2023)Novel deep neural network based stress detection systemETLTC-ICETM2023 INTERNATIONAL CONFERENCE PROCEEDINGS: ICT Integration in Technical Education & Entertainment Technologies and Management10.1063/5.0181900(030013)Online publication date: 2023
  • (2022)Application of NLP and Machine Learning for Mental Health ImprovementInternational Journal of Engineering and Advanced Technology10.35940/ijeat.F3657.081162211:6(47-52)Online publication date: 30-Aug-2022
  • (2022)A Meta-learning based Stress Category Detection Framework on Social MediaProceedings of the ACM Web Conference 202210.1145/3485447.3512013(2925-2935)Online publication date: 25-Apr-2022
  • (2022)Stress-Coping Tweets Acquisition: A Two-phase Bootstrapping Method on Patterns and Semantic Features2022 International Conference on Technologies and Applications of Artificial Intelligence (TAAI)10.1109/TAAI57707.2022.00029(113-118)Online publication date: Dec-2022
  • (2022)Category-Aware Chronic Stress Detection on MicroblogsIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2021.309046726:2(852-864)Online publication date: Feb-2022
  • (2022)The Improvement of Stress Level Detection in Twitter: Imbalance Classification Using SMOTE2022 IEEE International Conference on Computing (ICOCO)10.1109/ICOCO56118.2022.10031684(294-298)Online publication date: 14-Nov-2022
  • (2022)Stress Identification in Online Social Networks2022 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW58026.2022.00063(427-434)Online publication date: Nov-2022
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