An hybrid deep learning approach for depression prediction from user tweets using feature-rich CNN and bi-directional LSTM

Kour, Harnain; Gupta, Manoj Kumar

doi:10.1007/s11042-022-12648-y

Cited by 68 publications

(31 citation statements)

References 60 publications

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“…Many common techniques related to linguistic features are used to perform the classification task such as using LIWC in social media (Morales et al, 2017;Loveys et al, 2018) and EHR (Bittar et al, 2021). Researchers used a variety of machine learning techniques that range from conventional methods such as SVM (Tadesse et al, 2019;Yazdavar et al, 2017) and LR (Yazdavar et al, 2017;Karmen et al, 2015), to deep learning techniques such as feedforward networks (Geraci et al, 2017), CNN and LSTM (Mumtaz and Qayyum, 2019;Kour and Gupta, 2022). Many recent work also explored the use of recent pre-trained language models to improve the depression classification task performance, such as BERT-CNN in (Rodrigues Makiuchi et al, 2019) and ALBERT (Owen et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Explaining Models of Mental Health via Clinically Grounded Auxiliary Tasks

Zirikly¹,

Dredze²

2022

Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

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Models of mental health based on natural language processing can uncover latent signals of mental health from language. Models that indicate whether an individual is depressed, or has other mental health conditions, can aid in diagnosis and treatment. A critical aspect of integration of these models into the clinical setting relies on explaining their behavior to domain experts. In the case of mental health diagnosis, clinicians already rely on an assessment framework to make these decisions; that framework can help a model generate meaningful explanations.In this work we propose to use PHQ-9 categories as an auxiliary task to explaining a social media based model of depression. We develop a multi-task learning framework that predicts both depression and PHQ-9 categories as auxiliary tasks. We compare the quality of explanations generated based on the depression task only, versus those that use the predicted PHQ-9 categories. We find that by relying on clinically meaningful auxiliary tasks, we produce more meaningful explanations.

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Section: Related Workmentioning

confidence: 99%

Explaining Models of Mental Health via Clinically Grounded Auxiliary Tasks

Zirikly¹,

Dredze²

2022

Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

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“…From Figure 8 , it can be perceived that number of epochs is varied from 10 to 100 and for each period time of convergence is plotted. In all iteration periods, the proposed method using CNN performs well as compared to existing system ( 32 ). It can be proved when the iteration reaches 50th period and during this case, the convergence is achieved at 5.77 s whereas the existing model ranges at 8.7 s and at 80th period convergence is attained.…”

Section: Resultsmentioning

confidence: 88%

“…However, a comparative analysis will not provide solutions to overcome the problems in health-related issues which indicates that a testing model must be placed during comparison state. To have a deep insight knowledge testing model comparisons are made ( 32 ) using long short-term memory in two different directions. In this bidirectional procedure, the neural networks are incorporated and the visualization approaches are built for extracting high-feature comparisons.…”

Section: Optimization Algorithm: Classification and Regression Develo...mentioning

confidence: 99%

Perception Exploration on Robustness Syndromes With Pre-processing Entities Using Machine Learning Algorithm

Kshirsagar

Manoharan²,

Shitharth

et al. 2022

Front. Public Health

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The majority of the current-generation individuals all around the world are dealing with a variety of health-related issues. The most common cause of health problems has been found as depression, which is caused by intellectual difficulties. However, most people are unable to recognize such occurrences in them, and no procedures for discriminating them from normal people have been created so far. Even some advanced technologies do not support distinct classes of individuals as language writing skills vary greatly across numerous places, making the central operations cumbersome. As a result, the primary goal of the proposed research is to create a unique model that can detect a variety of diseases in humans, thereby averting a high level of depression. A machine learning method known as the Convolutional Neural Network (CNN) model has been included into this evolutionary process for extracting numerous features in three distinct units. The CNN also detects early-stage problems since it accepts input in the form of writing and sketching, both of which are turned to images. Furthermore, with this sort of image emotion analysis, ordinary reactions may be easily differentiated, resulting in more accurate prediction results. The characteristics such as reference line, tilt, length, edge, constraint, alignment, separation, and sectors are analyzed to test the usefulness of CNN for recognizing abnormalities, and the extracted features provide an enhanced value of around 74%higher than the conventional models.

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“…The remaining datasets range in size from 300 to 500 rows. Tthere may be a limit to the number of tweets and reviews [ 12 ]. There are various hybrid sentiment analysis models.…”

Section: Proposed Modelmentioning

confidence: 99%

Deep Sentiment Analysis of Twitter Data Using a Hybrid Ghost Convolution Neural Network Model

Al-Abyadh

Iesa

Azeem³

et al. 2022

Computational Intelligence and Neuroscience

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Several problems remain, despite the evident advantages of sentiment analysis of public opinion represented on Twitter and Facebook. On complicated training data, hybrid approaches may reduce sentiment mistakes. This research assesses the dependability of numerous hybrid approaches on a variety of datasets. Across domains and datasets, we compare hybrid models to singles. Text tweets and reviews are included in our deep sentiment analysis learning systems. The support vector machine (SVM), Long Short-Term Memory (LSTM), and ghost model convolution neural network (CNN) are combined to get the hybrid model. The dependability and computation time of each approach were evaluated. On all datasets, hybrid models outperform single models when deep learning and SVM are combined. The traditional models were less trustworthy, and deep learning algorithms have recently shown their enormous promise in sentiment analysis. Linear transformations are used in feature maps to eliminate duplicate or related features. The ghost unit makes ghost features by taking away attributes that are both similar and duplicated from each intrinsic feature. LSTM produces higher results but takes longer to process, while CNN needs less hyperparameter adjusting and monitoring. The effectiveness of the integrated model varies depending on the work, and all performed better than the others. For hybrid deep sentiment analysis learning models, LSTM networks, CNNs, and SVMs are needed. Hybrid models are used to compare SVM, LSTM, and CNN, and we tested each method’s accuracy and errors. Deep learning-SVM hybrid models improve sentiment analysis accuracy. Experimental results have shown the accuracy of the proposed model shown 91.3 percent and 91.5 percent for datasets type 1 and 8, respectively.

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An hybrid deep learning approach for depression prediction from user tweets using feature-rich CNN and bi-directional LSTM

Cited by 68 publications

References 60 publications

Explaining Models of Mental Health via Clinically Grounded Auxiliary Tasks

Explaining Models of Mental Health via Clinically Grounded Auxiliary Tasks

Perception Exploration on Robustness Syndromes With Pre-processing Entities Using Machine Learning Algorithm

Deep Sentiment Analysis of Twitter Data Using a Hybrid Ghost Convolution Neural Network Model

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