Negative emotions detection on online mental-health related patients texts using the deep learning with MHA-BCNN model

Dheeraj, Kodati; Ramakrishnudu, Tene

doi:10.1016/j.eswa.2021.115265

Cited by 36 publications

(8 citation statements)

References 51 publications

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“…In the first phase, an initial database of 129 movie clips was constructed by seven members of our research team. In this process, in accordance with the requirements of EMDB [10], the following three criteria were referenced: (a) stability of video content; (b) the continuous presence of characters in the scene except neutral material; (c) positive and negative emotions not being elicited at the same time. Each clip of 150 seconds or so was edited to ensure the consistency of the acting scene and emotional content throughout its duration.…”

Section: Stimulus Selectionmentioning

confidence: 99%

The increasing types of negative emotion impose an extra pressure on the accuracy of emotion recognition

Wang

Pei

Zhang³

et al. 2022

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022)

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Negative emotion recognition plays a vital role in the field of affective computing. Recent studies reveal fuzzy boundaries among negative emotions that affect emotion recognition performance. However, to the best of our knowledge, there is no formal research to explore the impact of increasing negative emotions on the classification performance of emotion recognition. In this study, a novel dataset was designed and established with three sessions, which respectively include happy, calm fear (session1), session1 + sad (session2), and session 2+angry (session3). All analysis was based on differential entropy (DE) of EEG and heart rate variability (HRV) of ECG. The results show that the similarity of features of different emotions has increased significantly with more and more negative emotions added in the experiment process. This means that the increasing negative emotions video clips in the experimental paradigm affect emotion recognition performance to a great extent. The balanced types of emotional stimuli should be considered in the subsequent design for negative emotion recognition to avoid the adverse impact of increasing negative emotions.

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Section: Stimulus Selectionmentioning

confidence: 99%

The increasing types of negative emotion impose an extra pressure on the accuracy of emotion recognition

Wang

Pei

Zhang³

et al. 2022

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022)

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“…To tackle the problems of machine learning based methods, an increasing number of researchers try to apply deep learning based methods to tasks of NLP, such as sentiment analysis [1,13,29] and emotion classification [30][31][32][33][34][35] . As deep learning models combined with graphs, such as GNN [36] and graph convolutional networks (GCN) [37] , have been proposed, an increasing number of researchers utilize them for emotion classification [38][39][40] .…”

Section: Related Workmentioning

confidence: 99%

$p$-Norm Broad Learning for Negative Emotion Classification in Social Networks

Chen

Peng

Zeng

et al. 2022

Big Data Min. Anal.

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Negative emotion classification refers to the automatic classification of negative emotion of texts in social networks. Most existing methods are based on deep learning models, facing challenges such as complex structures and too many hyperparameters. To meet these challenges, in this paper, we propose a method for negative emotion classification utilizing a Robustly Optimized BERT Pretraining Approach (RoBERTa) and p-norm Broad Learning (p-BL). Specifically, there are mainly three contributions in this paper. Firstly, we fine-tune the RoBERTa to adapt it to the task of negative emotion classification. Then, we employ the fine-tuned RoBERTa to extract features of original texts and generate sentence vectors. Secondly, we adopt p-BL to construct a classifier and then predict negative emotions of texts using the classifier. Compared with deep learning models, p-BL has advantages such as a simple structure that is only 3-layer and fewer parameters to be trained. Moreover, it can suppress the adverse effects of more outliers and noise in data by flexibly changing the value of p. Thirdly, we conduct extensive experiments on the public datasets, and the experimental results show that our proposed method outperforms the baseline methods on the tested datasets.

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“…e detailed process is shown in formula (8) and formula (9). We initially utilize the capabilities of several convolutional layers in the VGG16 convolutional base to learn the sentiment representation of images during the training of the CNN model.…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

“…Deep learning has been one of the key directions of academic research recently. Additionally, it has made significant strides in the research of college students' affective cognition [8][9][10]. Many scholars have proposed to use deep neural network models such as CNN [11] and Bi-LSTM [12] to analyze and train the influencing factors affecting students' affective cognition problems and establish analytical models to classify and predict students' affective cognition problems.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Affective Cognition Analysis Model for College Students Using a Deep Convolution Neural Network and Deep Features

Feng

2022

Computational Intelligence and Neuroscience

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Currently, under the impact of the COVID-19, college students are facing increasingly elevated employment pressure and higher education pressure. This can easily cause a huge psychological burden on them, causing affective cognition problems such as anxiety and depression. In the long run, this is not conducive to students’ physical and mental health, nor is it conducive to the healthy development of the school and even the whole society. Therefore, it is imperative to build a novel adaptive affective cognition analysis model for college students. In particular, in the context of smart cities and smart China, many universities have opened the smart campus mode, which provides a huge data resource for our research. Due to problems of the low real-time evaluation and single data source in traditional questionnaire evaluation methods, evaluation errors are prone to occur, which in turn interferes with subsequent treatment. Therefore, for the purpose of alleviating the above deficiencies and improving the efficiency and accuracy of the affective cognition analysis model of college students, this paper studies the adaptive affective cognition analysis method of college students on basis of deep learning. First, because students’ psychological problems are often not sudden, on the contrary, most of these abnormalities will leave traces in their daily activities. Therefore, this paper constructs a multisource dataset with the access control data, network data, and learning data collected from the smart campus platform to describe the affective cognition status of students. Second, the multisource dataset is divided into two categories: image and text, and the CNN model is introduced to mine the psychological characteristics of college students, so as to provide a reference for the subsequent affective cognition state assessment. Finally, simulation tests are developed to confirm the viability of the technique suggested in this research. The experiments demonstrate that the accuracy of the assessment model is significantly increased because it can fully reflect the heterogeneity and comprehensiveness of the data. This also highlights that the new method has a wide range of potential applications in the modern campus setting and is also helpful in fostering the accuracy and depth of college students’ work on their affective cognition.

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Negative emotions detection on online mental-health related patients texts using the deep learning with MHA-BCNN model

Cited by 36 publications

References 51 publications

The increasing types of negative emotion impose an extra pressure on the accuracy of emotion recognition

The increasing types of negative emotion impose an extra pressure on the accuracy of emotion recognition

$p$-Norm Broad Learning for Negative Emotion Classification in Social Networks

A Novel Adaptive Affective Cognition Analysis Model for College Students Using a Deep Convolution Neural Network and Deep Features

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