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• W3169183224 abstract "Mining the emotions in the text related to mental health-care oriented is a challenging aspect, especially dealing with a long-text sequence of data. The extraction of emotions depends upon the various psychological depression factors like negative and ambiguity. Identifying these factors is the most perplexing task for every psychiatrist to treat their patients. Our study includes the deep learning (DL) models with global vector representations (GloVe) embeddings to capture the text sequence of data. We proposed a model multi-head attention with bidirectional long short-term memory and convolutional neural network (MHA-BCNN) is a pre-eminent mechanism that outperforms better than past research works for capturing the negative text-based emotions. In this paper, by using DL extracted the various negative mental-health emotions like addiction, anxiety, depression, insomnia, stress, and obsessive cleaning disorder (OCD). By using the GloVe embeddings and handled the ambiguity factors like multiple emotion words in a certain sequence. As we proposed a vigorous appliance in our research to capture and hoard the long-term dependencies. We extracted the questions related to mental health issues were posted by the patients in an online mental healthcare-oriented platform. We efficaciously handled both negative and ambiguity factors at the document level. Our suggested exemplary MHA-BCNN surmounts various aspects from preceding research works and ensued preeminent performance. Experimental results show that our proposed framework MHA-BCNN outperformed than the erstwhile research works." @default.
• W3169183224 created "2021-06-22" @default.
• W3169183224 creator A5010440061 @default.
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• W3169183224 date "2021-11-01" @default.
• W3169183224 modified "2023-10-01" @default.
• W3169183224 title "Negative emotions detection on online mental-health related patients texts using the deep learning with MHA-BCNN model" @default.
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• W3169183224 doi "https://doi.org/10.1016/j.eswa.2021.115265" @default.
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