Detection of Stress and Relaxation Magnitudes for Tweets

Pillai, Reshmi Gopalakrishna; Thelwall, Mike; Orăsan, Constantin

doi:10.1145/3184558.3191627

Cited by 27 publications

(15 citation statements)

References 25 publications

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“…Tweet level emotion detection reflects the instant emotions expressed by a particular tweet which also reflect mental health disorder such as depression or post-traumatic stress disorder (PTSD). In paper [1] the author Mike Thelwall implemented a WSD technology as a pre-processing stage and further works on lexicon based stress or relaxation method which improves the accuracy of TensiStrength. In this used the dataset of "1000' tweets with word "Fine" which reflects ambiguous meaning in different situation of sentences.…”

Section: Media Content Moving To Research On Social Mediamentioning

confidence: 99%

“…Relaxation gives health whereas stress affects your health [1]. In the present day, situation Stress is most rapidly increasing.…”

Section: Introductionmentioning

confidence: 99%

“…The limitation of this method is, it is time-consuming and a bit expensive. The other and the recent method to stress detection is social media [1]. Stress detection is possible through social media, Based on one's write-up on social media, one's reacting on a particular problem on social media, one's liking and disliking help to detect stress.…”

Section: Introductionmentioning

confidence: 99%

“…TensiStrength works more accurate than sentiment analysis program SentiStrength. This algorithm labels each and every tweet or sentence as stressed or relaxed [1]. Sentiment analysis system either use a lexicon or machine learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Detection and Analysis of Stress using Machine Learning Techniques

Baheti¹,

Kinariwala²

2019

IJEAT

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Every year tens of millions of people suffer from depression and few of them get proper treatment on time. So, it is crucial to detect human stress and relaxation automatically via social media on a timely basis. It is very important to detect and manage stress before it goes into a severe problem. A huge number of informal messages are posted every day in social networking sites, blogs and discussion forums. This paper describes an approach to detect the stress using the information from social media networking sites, like tweeter.This paper presents a method to detect expressions of stress and relaxation on tweeter dataset i.e. working on sentiment analysis to find emotions or feelings about daily life. Sentiment analysis works the automatic extraction of sentiment related information from text. Here using TensiStrengthframework for sentiment strength detection on social networking sites to extract sentiment strength from the informal English text. TensiStrength is a system to detect the strength of stress and relaxation expressed in social media text messages. TensiStrength uses a lexical approach and a set of rules to detect direct and indirect expressions of stress or relaxation. This classifies both positive and negative emotions based on the strength scale from -5 to +5 indications of sentiments. Stressed sentences from the conversation are considered &categorised into stress and relax. TensiStrength is robust, it can be applied to a widevarietyofdifferent social web contexts. Theeffectiveness of TensiStrength depends on the nature of the tweets.In human being there is inborn capability to differentiate the multiple senses of an ambiguous word in a particular context, but machine executes only according to the instructions. The major drawback of machine translation is Word Sense Disambiguation. There is a fact that a single word can have multiple meanings or "senses." In the pre-processing partof-speech disambiguation is analysed and the drawback of WSD overcomes in the proposed method by unigram, bigram and trigram to give better result on ambiguous words. Here, SVM with Ngram gives better resultPrecision is65% and Recall is 67% .But, the main objective of this technique is to find the explicit and implicit amounts of stress and relaxation expressed in tweets. Keywords: Stress Detection, Data Mining, TensiStrength, word sense disambiguation.

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Section: Media Content Moving To Research On Social Mediamentioning

confidence: 99%

“…Relaxation gives health whereas stress affects your health [1]. In the present day, situation Stress is most rapidly increasing.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection and Analysis of Stress using Machine Learning Techniques

Baheti¹,

Kinariwala²

2019

IJEAT

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“…In the literature, good efforts have been made in stress detection. Questionnaires, psychological interviews, physical equipments, physiological signals, as well as social media, are used for stress detection [11,13,21,35].…”

Section: Introductionmentioning

confidence: 99%

Leverage Social Media for Personalized Stress Detection

Wang

Zhang

Cao

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Timely detection of stress is desirable to address the increasingly serious stress problem. Thanks to the rich linguistic expressions and complete historical records on social media, achieving personalized stress detection through social media is feasible and prominent. We construct a three-leveled framework, aiming at personalized stress detection based on social media. The three-leveled framework learns the personalized stress representations following an increasingly detailed processing, i.e., from the generic mass level, group level, to the final individual level. The first mass-level focuses on mining the generic stress representations from people's linguistic and visual posts with a two-layer attention mechanism. The second group-level adopts the graph neural network to learn the groupwise characteristics of the group where an individual belongs to. The third individual-level analyzes and incorporates individual's personality traits into stress detection. The performance study on the 2,059 microblog users shows that our proposed method can achieve over 90% in detection accuracy. Furthermore, the extended experiment on a harder personalized sub-dataset demonstrates that our method works better in distinguishing personalized expressions with different latent meanings. CCS CONCEPTS • Applied computing → Psychology; • Human-centered computing → Social media; • Information systems → Sentiment analysis.

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A Systematic Review of Artificial Intelligence and Mental Health in the Context of Social Media

Chen

Genc

2022

Lecture Notes in Computer Science

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Detection of Stress and Relaxation Magnitudes for Tweets

Cited by 27 publications

References 25 publications

Detection and Analysis of Stress using Machine Learning Techniques

Detection and Analysis of Stress using Machine Learning Techniques

Leverage Social Media for Personalized Stress Detection

A Systematic Review of Artificial Intelligence and Mental Health in the Context of Social Media

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