Emotions ontology for collaborative modelling and learning of emotional responses

Gil, Rosa; Virgili-Gomá, Jordi; Garcı́a, Roberto; Mason, Cindy

doi:10.1016/j.chb.2014.11.100

Cited by 48 publications

(28 citation statements)

References 21 publications

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“…These proposed systems aim to explore or improve EEG-based emotion recognition systems. [2,39,41,42,49,50,57,61,63,92,104,108,109,117,131,136,149,152,157,173,174,185,186,189,191,[195][196][197][198][199][200][201][202][203][204][205][206][207][208][209]217,219,[223][224][225]229,[262][263][264][265][266]<...>…”

Section: Monitoringmentioning

confidence: 99%

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

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Section: Monitoringmentioning

confidence: 99%

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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“…Sentiment classification of those valuable forum posts can assist instructors to make interventions and guiding instructions to improve learning performance. Lacking monitoring on learners' sentiments may lead to high dropout rates of courses [5]. Moreover, the forum posts may contain significant sentiment orientation for institutions to incorporate changes to improve their course quality, teaching strategies, and other academic elements [6].…”

Section: Introductionmentioning

confidence: 99%

Co-Training Semi-Supervised Deep Learning for Sentiment Classification of MOOC Forum Posts

et al. 2019

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Sentiment classification of forum posts of massive open online courses is essential for educators to make interventions and for instructors to improve learning performance. Lacking monitoring on learners' sentiments may lead to high dropout rates of courses. Recently, deep learning has emerged as an outstanding machine learning technique for sentiment classification, which extracts complex features automatically with rich representation capabilities. However, deep neural networks always rely on a large amount of labeled data for supervised training. Constructing large-scale labeled training datasets for sentiment classification is very laborious and time consuming. To address this problem, this paper proposes a co-training, semi-supervised deep learning model for sentiment classification, leveraging limited labeled data and massive unlabeled data simultaneously to achieve performance comparable to those methods trained on massive labeled data. To satisfy the condition of two views of co-training, we encoded texts into vectors from views of word embedding and character-based embedding independently, considering words' external and internal information. To promote the classification performance with limited data, we propose a double-check strategy sample selection method to select samples with high confidence to augment the training set iteratively. In addition, we propose a mixed loss function both considering the labeled data with asymmetric and unlabeled data. Our proposed method achieved a 89.73% average accuracy and an 93.55% average F1-score, about 2.77% and 3.2% higher than baseline methods. Experimental results demonstrate the effectiveness of the proposed model trained on limited labeled data, which performs much better than those trained on massive labeled data.

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“…The experimental results on the real-life moods posts demonstrated that their methodology outperformed conventional multi-view semi-supervised emotion recognition methods. With the same intention, but under the ontology perspective, Gil, Virgili-Gomá, García, and Mason (2015) applied ontologies in the context of Emotional Common Sense, collaboratively collecting emotion common sense and modelling it with an ontology named EmotionsOnto. Their experiments were conducted to automatically measure users' emotional states using Brain Computer Interfaces.…”

Section: Related Workmentioning

confidence: 99%

A MOOC on Approaches to Machine Translation

Costa-jussà

Formiga²,

Torrillas

et al. 2015

IRRODL

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This paper describes the design, development, and analysis of a MOOC entitled "Approaches to Machine Translation: Rule-based, statistical and hybrid", and provides lessons learned and conclusions to be taken into account in the future. The course was developed within the Canvas platform, used by recognized European universities. It contains video-lectures, quizzes, and laboratory assignments. Evaluation was through on-line quizzes, programming assignments assessed by means of a specific code evaluation, and peer-to-peer strategies. This MOOC allowed people from various fields to be introduced to the theory and practice of Machine Translation. It also enabled us to internationally publicize various tools developed at the Universitat Politècnica de Catalunya. Introduction 175In the context of Natural Language Processing (NLP), Machine Translation (MT) brings together varied communities from linguists to engineers. From the Universitat Politècnica de Catalunya (UPC), with more than 12 years of experience in the topic, we launched a MOOC that provides an overview of the basis for the most popular MT systems. Our main motivations for developing this course are listed as follows:• To improve the content and digital resources of MT.• To adapt an MT course to an international public.• To propose a course on a complete platform that is used by large European universities.• To use and publicize the open-source NLP tools developed at the UPC that are useful for the MT community.Although the MOOC we are describing is very specific to MT, it was well received, with over 772 students registering from all around the globe. The most relevant characteristic of the MOOC presented is its development under a low-resource scenario and its far-reaching marketing strategy using in-house low-budget tools. The MOOC was set up on the well-known Canvas platform, but customized to UPC requirements. In this sense, this paper extends our ongoing previous work (Costa-jussà, Formiga, Petit & Fonollosa, 2014) and describes the complete course, focusing on how the low-resource MOOC on the topic of MT has been developed, together with a deep analysis of the output of the course mainly in terms of participation. Therefore, this paper can be useful to professors and/or researchers that want to develop a MOOC on a fresh topic and independently from the big platforms. Among the different ideas that the reader can use, we provide details of how to: constitute a multidisciplinary group of people to develop the course; promote and motivate the course; proceed step-by-step for recording and preparing evaluation materials; and design and analyze the pre-and post-course surveys.The rest of this paper is organized as follows. Section 2 depicts a brief description in both coursesof MT with open material and already existing MOOCs under similar fields. Section 3 presents the course topic and contents. Section 4 describes the procedure for recording the video lectures and the promotional video, the functionalities of the Canvas platform, the MOOC team...

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Emotions ontology for collaborative modelling and learning of emotional responses

Cited by 48 publications

References 21 publications

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Co-Training Semi-Supervised Deep Learning for Sentiment Classification of MOOC Forum Posts

A MOOC on Approaches to Machine Translation

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