A Deep Learning-Based Approach for Multi-Label Emotion Classification in Tweets

Jabreel, Mohammed; Moreno, Antonio

doi:10.3390/app9061123

Cited by 124 publications

(68 citation statements)

References 29 publications

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“…The most famous usage of this analysis is the detection of sentiment on Twitter. In recent work, Mohammed et al proposed an automatic system called a Binary Neural Network (BNet) to classify multi-label emotions by using deep learning for Twitter feeds [18]. They conducted their work on emotion analysis with the co-existence of multiple emotion labels in a single instance.…”

Section: Human Emotionsmentioning

confidence: 99%

Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges

Lim

Mountstephens

Teo

2020

Sensors

162

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The ability to detect users’ emotions for the purpose of emotion engineering is currently one of the main endeavors of machine learning in affective computing. Among the more common approaches to emotion detection are methods that rely on electroencephalography (EEG), facial image processing and speech inflections. Although eye-tracking is fast in becoming one of the most commonly used sensor modalities in affective computing, it is still a relatively new approach for emotion detection, especially when it is used exclusively. In this survey paper, we present a review on emotion recognition using eye-tracking technology, including a brief introductory background on emotion modeling, eye-tracking devices and approaches, emotion stimulation methods, the emotional-relevant features extractable from eye-tracking data, and most importantly, a categorical summary and taxonomy of the current literature which relates to emotion recognition using eye-tracking. This review concludes with a discussion on the current open research problems and prospective future research directions that will be beneficial for expanding the body of knowledge in emotion detection using eye-tracking as the primary sensor modality.

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Section: Human Emotionsmentioning

confidence: 99%

Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges

Lim

Mountstephens

Teo

2020

Sensors

162

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“…Therefore, the proposed approach is a data analysis technique that should be useful to those who seek to use UGC to improve their communication or marketing strategies. Also, it should be useful to educational institutions that may be able to enhance their offerings by identifying needs and trends based on technologies previously investigated [52][53][54][55]. Figure 1 shows the training process of the SVM algorithm and the classification according to the feelings that (a) represents the training process of a sentiment analysis algorithm with a feature extractor and a machine learning algorithm, and (b) represents the prediction process of a sentiment analysis algorithm with a feature extractor and a classifier model [2].…”

Section: Knowledge-based Methods To Extract Insights From Ugcmentioning

confidence: 99%

How to Extract Meaningful Insights from UGC: A Knowledge-Based Method Applied to Education

2019

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New analysis and visualization techniques are required to glean useful insights from the vast amounts of data generated by new technologies and data sharing platforms. The aim of this article is to lay a foundation for such techniques so that the age of big data may also be the age of knowledge, visualization, and understanding. Education is the keystone area used in this study because it is deeply affected by digital platforms as an educational medium and also because it deals mostly with digital natives who use information and communication technology (ICT) for all manner of purposes. Students and teachers are therefore a rich source of user generated content (UGC) on social networks and digital platforms. This article shows how useful knowledge can be extracted and visualized from samples of readily available UGC, in this case the text published in tweets from the social network Twitter. The first stage employs topic-modeling using LDA (latent dirichlet allocation) to identify topics, which are then subjected to sentiment analysis (SA) using machine-learning (developed in Python). The results take on meaning through an application of data mining techniques and a data visualization algorithm for complex networks. The results obtained show insights related to innovative educational trends that practitioners can use to improve strategies and interventions in the education sector in a short-term future.

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“…Results showed that the more sensitive an individual is, the more health care is considered. Jabreel et al [23] proposed a multi-labeled sentiment analysis on social media with deep learning. They restructured the multi-label problem to a single binary issue, then applied deep learning.…”

Section: Related Workmentioning

confidence: 99%

A Sequential Emotion Approach for Diagnosing Mental Disorder on Social Media

2020

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Mental disorder has been affecting numerous individuals; however, mental health care is in a passive state where only a minority of individuals actively seek professional help. Due to the rapid development of social networks, individuals accustomed to expressing their raw feelings on social media include patients who are suffering great pain from mental disorders. To distinguish individuals who merely feel sad and others who have mental disorders, the symptoms of mental disorder are taken into consideration. These symptoms constantly arise as a regular pattern like shifting of emotions or repeating of one representative emotion during a certain time. We proposed a Mental Disorder Identification Model (MDI-Model) to identify the four most commonly occurring mental disorders in the world: anxiety disorder, bipolar disorder, depressive disorder, and obsessive-compulsive disorder (OCD). The MDI-Model compares the sequential emotion pattern from users to identify mental disorders to detect those who are in a high risk. Tweets of diagnosed mental disorder users were analyzed to evaluate the accuracy of the MDI-Model, furthermore, the tweets of users from six different occupations were analyzed to verify the precision and predict the tendency of mental disorder among the different occupations. Results show that the MDI-Model can efficiently diagnose users with high precision in different mental statuses as severe, moderate, and mild stage, or tendency of mental disorder and mentally healthy status.

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A Deep Learning-Based Approach for Multi-Label Emotion Classification in Tweets

Cited by 124 publications

References 29 publications

Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges

Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges

How to Extract Meaningful Insights from UGC: A Knowledge-Based Method Applied to Education

A Sequential Emotion Approach for Diagnosing Mental Disorder on Social Media

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