Deep Bag-of-Sub-Emotions for Depression Detection in Social Media

Lara, Juan Sebastián; Aragón, Mario Ezra; González, Fabio A.; Montes-y-Gómez, Manuel

doi:10.1007/978-3-030-83527-9_5

Cited by 11 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zogan et al [25] implemented a feature selection process that includes positive, negative, and neutral emoji frequencies and valence, arousal, and dominance scores for depression detection. More recent studies have used machine and deep learning to fine-tune contextual embeddings using mental health classification as a downstream task [3,4,13,15,26]. In [26], an Emotion Understanding Network (EUN) is proposed where positive and negative word embeddings are learned separately by separate attention networks, which are later combined with a Semantic Understanding Network (SUN) for depression detection.…”

Section: Social Media Mental Health Classificationmentioning

confidence: 99%

See 1 more Smart Citation

MM-EMOG: Multi-Label Emotion Graph Representation for Mental Health Classification on Social Media

Cabral,

Han,

Poon

et al. 2024

Robotics

View full text Add to dashboard Cite

More than 80% of people who commit suicide disclose their intention to do so on social media. The main information we can use in social media is user-generated posts, since personal information is not always available. Identifying all possible emotions in a single textual post is crucial to detecting the user’s mental state; however, human emotions are very complex, and a single text instance likely expresses multiple emotions. This paper proposes a new multi-label emotion graph representation for social media post-based mental health classification. We first construct a word–document graph tensor to describe emotion-based contextual representation using emotion lexicons. Then, it is trained by multi-label emotions and conducts a graph propagation for harmonising heterogeneous emotional information, and is applied to a textual graph mental health classification. We perform extensive experiments on three publicly available social media mental health classification datasets, and the results show clear improvements.

show abstract

Section: Social Media Mental Health Classificationmentioning

confidence: 99%

“…Recent studies incorporate emotions into mental health classification by fine-tuning pre-trained embeddings through a single emotion classification task [3,4]. Due to the complexity of human emotions, it is very likely that multiple emotions are expressed by a single textual post and that those emotions can be correlated.…”

Section: Introductionmentioning

confidence: 99%

MM-EMOG: Multi-Label Emotion Graph Representation for Mental Health Classification on Social Media

Cabral,

Han,

Poon

et al. 2024

Robotics

View full text Add to dashboard Cite

show abstract

“…Chiong et al [39] proposed text preprocessing and text-based feature method for depression detection and proved the universality of their approach through cross-database experiments. Lara et al [40] proposed the DeepBoSE model, which can extract the lexical sentiment information of user posts and utilize the attributes of deep learning models while retaining interpretability. Agirrezabal [41] et al integrated pre-trained BERT, RoBERTa, and XLNET models to extract text features and vote for depression detection.…”

Section: A Depression Detection Of Social Media Users Based On Single...mentioning

confidence: 99%

TAMFN: Time-Aware Attention Multimodal Fusion Network for Depression Detection

Zhou

Liu

Shangguan

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

In recent years, with the widespread popularity of the Internet, social media has become an indispensable part of people's lives. People regard online social media as an essential tool for interaction and communication. Due to the convenience of data acquisition from social media, mental health research on social media has received a lot of attention. The early detection of psychological disorder based on social media can help prevent further deterioration in at-risk people. In this paper, depression detection is performed based on non-verbal (acoustics and visual) behaviors of vlog. We propose a time-aware attentionbased multimodal fusion depression detection network (TAMFN) to mine and fuse the multimodal features fully. The TAMFN model is constructed by a temporal convolutional network with the global information (GTCN), an intermodal feature extraction (IFE) module, and a time-aware attention multimodal fusion (TAMF) module. The GTCN model captures more temporal behavior information by combining local and global temporal information. The IFE module extracts the early interaction information between modalities to enrich the feature representation. The TAMF module guides the multimodal feature fusion by mining the temporal importance between different modalities. Our experiments are carried out on D-Vlog dataset, and the comparative experimental results report that our proposed TAMFN outperforms all benchmark models, indicating the effectiveness of the proposed TAMFN model.

show abstract

“…The technique attained an AC of 90.27%. Lara et al (2021) suggested an approach called deep bag‐of‐sub‐emotions (DeepBoSE) for DD of SM data. Initially, the bag of features representation was computed for the input SM data, which contained emotional information.…”

Section: Related Workmentioning

confidence: 99%

A deep learning approach for the depression detection of social media data with hybrid feature selection and attention mechanism

Bhuvaneswari¹,

Prabha²

2023

Expert Systems

View full text Add to dashboard Cite

Depression is a severe mental health issue. The user-generated content on social media (SM) is growing nowadays. Some computational approaches have been proposed for detecting depression based on users' SM data. However, because of the use of formal language, short range of words and misspellings in the SM data, depression detection (DD) is a challenging task. This paper proposes a novel deep learning (DL) technique for performing DD of the SM data with the help of the hybrid feature selection (FS) mechanism. Initially, two publicly available datasets containing user tweets are collected for implementing the proposed research model. Then the collected datasets are preprocessed for further processing. The preprocessing phase

show abstract

Deep Bag-of-Sub-Emotions for Depression Detection in Social Media

Cited by 11 publications

References 17 publications

MM-EMOG: Multi-Label Emotion Graph Representation for Mental Health Classification on Social Media

MM-EMOG: Multi-Label Emotion Graph Representation for Mental Health Classification on Social Media

TAMFN: Time-Aware Attention Multimodal Fusion Network for Depression Detection

A deep learning approach for the depression detection of social media data with hybrid feature selection and attention mechanism

Contact Info

Product

Resources

About