Predicting the language of depression from multivariate twitter data using a feature‐rich hybrid deep learning model

Abstract

Depression is a clinical entity that might be difficult for a psychiatrist to diagnose it effectively on time. A depressed person usually suffers from distress and anxiety, leading to serious consequences if not diagnosed early. Social media platforms facilitate users to exchange ideas and dialogs, resulting in the collection of a huge volume of data. Analyzing user's online behavior to categorize depression is a challenging task for researchers. This motivated researchers to investigate machine learning, deep learning, and natural language processing techniques supporting research related to depression prediction. The dataset used in the study is a large-scale Twitter dataset. This article aims to investigate a hybrid CNN-LSTM deep learning model with the Word2Vec feature extraction technique for classifying depressive sentiments from Twitter data. By using TF-IDF, PCA, and Word2Vec approaches, this model utilizes significant linguistic features present within the text. The proposed model is evaluated on four benchmark datasets and its efficiency is compared with four traditional machine learning models. Moreover, the proposed model's performance is compared to three deep learning-based hybrid models. The proposed model showed comparable performance with the hybrid deep learning-based models and outperformed state-of-the-art machine learning techniques with an accuracy of 96.78% and an MSE score of 3.21.

Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning

“…The results showed that deep learning is more accurate in identifying DD and its risk factors compared to traditional machine learning by ranking the key factors of veterans and capturing the hidden pattern multilayer network structure in the data to obtain better classification performances. Kour et al [49] combined feature extraction techniques and a hybrid deep learning model of CNN-LSTM for depression classification and compared it with four traditional machine learning models for efficiency comparison, and showed that the recognition accuracy on the benchmark dataset reached 96.78%, which is better than the state-of-the-art traditional machine learning techniques. Deep learning, with its technological advantages, has a stronger predictive ability in DD diagnosis [50]; therefore, this study advocates for the use of deep learning algorithms for the automatic diagnosis of DD.…”

Inferring Depression and Its Semantic Underpinnings from Simple Lexical Choices

“…It is well acknowledged that language use and language processing are highly reflective of individual differences in personality traits [8][9][10][11][12], gender [11], mood [13,14], stance detection [15][16][17], and demographic characteristics [18][19][20][21]). Differences in language use and processing have further proved to be effective markers of psychiatric symptoms, particularly in the case of depression [22][23][24][25], where even the most conservative classification models perform comparably to the standard of validated self-report scales of symptom severity [24] and clinical assessment [22]. This suggests that language features capture a substantial portion of depression symptom variance.…”

Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts

“…Specifically, in mental health, predictive models have successfully been applied [12,63]. Exemplary contributions include the detection of depression [29], suicidal mental tendencies [21], bipolar disorders [24] and other mental disorders [20]. Moreover, the models proposed by [38] can uncover patterns of anorexia in datasets obtained from social networks.…”