Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

Babu, Nirmal Varghese; Kanaga, E. Grace Mary

doi:10.1007/s42979-021-00958-1

Cited by 155 publications

(75 citation statements)

References 101 publications

(326 reference statements)

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“…For example, the authors in [ 9 , 10 ] obtained a large amount of Twitter data, used the CES-D scale to get the user's depression state label, and analyzed the user's social network behavior data for feature extraction to construct a depression detection model, proving that Twitter data can be used to detect whether a user has depression. The authors in [ 11 ] used data from multiple blog platforms such as Yahoo Japan and Livedoor, combined with Japanese-specific language features for feature extraction. They used machine learning methods to build a depression detection model, proving that blog data can be used to detect depressed users.…”

Section: Related Workmentioning

confidence: 99%

Identification and Classification of Depressed Mental State for End-User over Social Media

Kumar

Thakare²,

Bhende³

et al. 2022

Computational Intelligence and Neuroscience

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In researching social network data and depression, it is often necessary to manually label depressed and non-depressed users, which is time-consuming and labor-intensive. The aim of this study is that it explores the relationship between social network data and depression. It can also contribute to detecting and identifying depression. Through collecting and analyzing college students’ microblog social data, a preliminary screening algorithm for college students’ suspected depression microblogs based on depression keywords, and semantic expansion is researched; a comprehensive lexical grammar was proposed. This research provided has a preliminary screening method based on depression keywords and semantic expansion for college students’ suspected depression microblogs, with a screening accuracy. This method forms a depression keyword table by constructing the basic keyword table and the semantic expansion based on the word embedding learning model Word2Vec. Finally, the word table is used to calculate the semantic similarity of the tested microblogs and then identify whether it is a suspected depression microblog. The experimental results on the microblog dataset of college students show that the comprehensive lexical method is better than the SDS questionnaire segmentation method and the expert lexical method in terms of screening accuracy; the comprehensive lexical approach can quickly and automatically screen out a tiny proportion of suspected doubts from a large number of college students’ microblogs. Depression Weibo can reduce the workload of experts’ annotation, improve annotation efficiency, and provide a suitable data processing basis for the subsequent accurate identification (classification problem) of patients with depression.

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Section: Related Workmentioning

confidence: 99%

Identification and Classification of Depressed Mental State for End-User over Social Media

Kumar

Thakare²,

Bhende³

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…Babu N V reviewed the use of various artificial intelligence techniques for sentiment analysis of social media data to detect fear or depression. In this investigation, social media data consisting of text, emojis, and emojis were found by optical means to be used for emotion recognition using various artificial information, but his research did not explain the timeliness of such research system attention [ 6 ]. Hamamoto R attempted to study this work using genomic medicine to elucidate the pathogenesis of diseases such as cancer.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Public View of Public Health Emergencies Based on Artificial Intelligence Data

Zhang¹,

Chu-ke²,

Kim

et al. 2022

Journal of Environmental and Public Health

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In the current environment where the network and the real society are intertwined, the network public view of public emergencies has involved in reality and altered the ecology of communal public views in China. A new online court of influence has been created, and it affected the trend of events. As the main type of public emergencies, public health emergencies are directly related to people’s health and life insurance. Therefore, the public often pays special attention. At present, correct media guidance plays an irreplaceable and important role in calming people’s hearts and stabilizing social order. If news and public view are left unchecked, it is likely to cause panic among the people. However, in reality, public view research has always been a research object that is difficult to intelligentize and quantify. Based on such a realistic background, the article conducts a research on public view of public health emergencies based on artificial intelligence data analysis. This study designs an expert system for network public view and optimizes the algorithm for the key problem: SFC deployment. Finally, the system was put into real news and public opinion research on new coronavirus epidemic prevention, and experimental tests were carried out. The experimental results have shown that in the new coronavirus incident, the nuclear leakage incident, and the epidemic prevention policy, the data obtained by the public through the Internet are 50%, 68.06%, and 64.35%, respectively. For the system function in this study, both ICSO and IPSO are far better than the optimization results of CSO and PSO. For most of the test functions, IPSO is better than ICSO’s optimization results, which better fulfills the needs of the research content. This study will make an in-depth analysis of the evolution process of online public opinion on public emergencies from the macro-, meso-, and micro-perspectives, in order to analyze the dissemination methods and internal evolution mechanism of various public emergencies of online public opinion, which provides countermeasures and suggestions for the government to guide and manage network public opinion.

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“…Interestingly, increased MSS was not only observed in the answer to the question about mood included in the interview, but also in the answer to other questions. This suggests that microdosing could be capable of inducing a state of positive mood, which generally affects verbal expression, and might be indicative of improved mental health (Babu & Kanaga, 2022). The same applies to increased verbosity, which could reflect more enthusiasm, motivation and energy during the acute effects of the microdose (Sanz et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Natural language signatures of psilocybin microdosing

Sanz

Cavanna

Müller

et al. 2022

Preprint

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Serotonergic psychedelics are being studied as novel treatments for mental health disorders and as facilitators of improved well-being, mental function and creativity. Recent studies have found mixed results concerning the effects of low doses of psychedelics (microdosing) on these domains. However, microdosing is generally investigated using instruments designed to assess larger doses of psychedelics, which might lack sensitivity and specificity for this purpose. Following a double-blind and placebo-controlled experimental design, we explored natural language as a resource to identify speech produced under the acute effects of psilocybin microdoses, focusing on variables known to be affected by higher doses: verbosity, semantic variability and sentiment scores. Except for semantic variability, these metrics presented significant differences between a typical active microdose of 0.5 g of psilocybin mushrooms and an inactive placebo condition. Moreover, machine learning classifiers trained using these metrics were capable of distinguishing between conditions with high accuracy (AUC close to 0.8). Our results constitute first proof that low doses of serotonergic psychedelics can be identified from unconstrained natural speech, with potential for widely applicable, affordable, and ecologically valid monitoring of microdosing schedules.

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Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

Cited by 155 publications

References 101 publications

Identification and Classification of Depressed Mental State for End-User over Social Media

Identification and Classification of Depressed Mental State for End-User over Social Media

[Retracted] Public View of Public Health Emergencies Based on Artificial Intelligence Data

Natural language signatures of psilocybin microdosing

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