2. Related Works
Studies such as Zaghouani [3] has looked into a big social media corpus for identifying youth depression. The author suggests developing a sentiment analysis-based linguistically annotated corpus to study teen online behaviour across the MENA region. The authors want to eventually compile a sizable user base with more precise self-reported sadness signals. A sentiment analysis and emotion recognition-based automated psychometric analyzer for healthcare has been looked at by Vij [4]. They asserted and came to the conclusion that the main objective of the proposed work is to develop a self-service medical kiosk or a psychometric analyzer with fast computational linguistics capabilities that can produce a brief, concise summary of the patient’s emotional health based on previous records, medications, and treatments. Almouzini et al. [5] looked into finding Arabic Twitter users that were depressed. They claimed to have developed a prediction model based on the identification of depressed individuals using an Arabic sentiment analysis employing supervised learning to assess whether a user’s tweet is depressed or not. They found that sad people are more socially isolated. Priya et al. [6] have suggested ML algorithms for predicting stress, depression, and anxiety in contemporary life. They claimed that in this study, ML algorithms were employed to evaluate five different levels of stress, depression, and anxiety. They discovered that random forest has the highest accuracy (91% and 89%). A research has been done by Feuston et al. [7] on how mental illness is expressed on Instagram. They explained how their individual histories, viewpoints, and experiences with mental illness and health had an impact on how they understood the findings. Murnane et al. [8] have suggested designing technology to support long-term mental health management social ecologies. They paid close attention to the patient’s perspective as well as the many viewpoints and experiences. A new class of collaborative informatics infrastructures and interfaces aimed at enabling the social ecologies of personal data activities will be built using the widely applicable design principles they gave. Pater et al. [9] have looked at a study of a case involving eating disorder patients who used digital self-harm indicators. Future studies, according to the report’s authors, might examine post-intervention data and contrast it with pre-intervention data to assess changes in patients’ online identity presentations. Among methods for identifying depression in college students, Xu et al. [10] suggested employing contextually-filtered characteristics and routing behaviour. In this paper, they present a unique association rule mining-based technique for automatically producing contextually filtered features that performs better than existing feature selection techniques for a depression diagnosis. Psycholinguistic patterns in social media texts have been presented by Trifan et al. [11], which aid in our understanding of depression. They are eager to discuss other psycholinguistic components with those who can shed light on them through clinical papers in a subsequent investigation. In a work that Mathur et al. [12] suggested, suicidal intent was estimated using temporal psycholinguistic clues. This study fills a gap by combining qualitative and quantitative approaches to examine the effects of enhancing text-based suicidal ideation identification.
There aren’t many statistics and publications about depression despite the fact that it’s a serious mental health problem. Problems with NLP are common today. The reason for this is likely a general lack of interest in the subject. Another problem is that because the topic is quite subjective, classifying such specific behavioural patterns may be challenging. The work by Losada and Crestani [2] offers excellent insight into this issue. Their dataset is the first to be used in research on language use and depression. The details of this dataset, which was also used in this investigation, are described in Section 3. An Early Risk Detection Error (ERDE) measure was established by Losada and Crestani [2] as a fresh evaluation statistic for their methodology. This metric is concerned with the speed at which affirmative circumstances can be found and the accuracy of assessments. In a fascinating study, Wang et al. [13] employed sentiment analysis to assess whether or not a user was depressed. It is advised that word and artificial regulations be used to determine each micro-depressive blog’s propensity. After that, a framework for detecting depression is created using the suggested approach and ten psychologically confirmed traits of depressed people. Since social networks have a lot of text information, many researchers are seeking to build models based on ever expanding data. In the years to come, using NLP with such a benefit to address the growing depression problem may be adequate to delve further into melancholy and provide doctors with fresh and intriguing information. Another excellent method for assessing the mental health and suicide risk of a community was provided by Benton et al. [14]. It has been demonstrated that gender modelling improves accuracy in tasks involving social media text. For 10 prediction tasks, the authors of these developed neural Multi-Task Learning (MTL) models. The outcomes of their model demonstrated that choosing the MTL thinks that employing the appropriate selection of auxiliary activities for a certain mental state might result in a significantly better model. For situations with the fewest data points, the model dramatically improves. The most significant finding for our purposes was that gender prediction does not adhere to the two aforementioned rules but rather improves performance as a measure of a supplemental task.