Natural language processing applied to mental illness detection: a narrative review

Zhang, Tianlin; Schoene, Annika Marie; Ji, Shaoxiong; Ananiadou, Sophia

doi:10.1038/s41746-022-00589-7

Cited by 170 publications

(120 citation statements)

References 201 publications

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“…Clinicians and health professionals also benefit from emotion analysis to understand public emotions and public health changes in perception of an intervention (ie, vaccine). Emotion detection systems have been used for alerting public health practitioners, for monitoring mental health patients [ 27 ], suicide prevention [ 28 ], and adverse drug reactions [ 29 ]. Some works utilized emotion-based features to specifically detect adverse drug reactions reported by users on social media, which can guide health professionals and pharmaceutical companies in making medications safer and advocating for patient safety [ 30 - 32 ].…”

Section: Introductionmentioning

confidence: 99%

Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis

Alhuzali¹,

Zhang²,

Ananiadou³

2022

J Med Internet Res

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Background In recent years, the COVID-19 pandemic has brought great changes to public health, society, and the economy. Social media provide a platform for people to discuss health concerns, living conditions, and policies during the epidemic, allowing policymakers to use this content to analyze the public emotions and attitudes for decision-making. Objective The aim of this study was to use deep learning–based methods to understand public emotions on topics related to the COVID-19 pandemic in the United Kingdom through a comparative geolocation and text mining analysis on Twitter. Methods Over 500,000 tweets related to COVID-19 from 48 different cities in the United Kingdom were extracted, with the data covering the period of the last 2 years (from February 2020 to November 2021). We leveraged three advanced deep learning–based models for topic modeling to geospatially analyze the sentiment, emotion, and topics of tweets in the United Kingdom: SenticNet 6 for sentiment analysis, SpanEmo for emotion recognition, and combined topic modeling (CTM). Results We observed a significant change in the number of tweets as the epidemiological situation and vaccination situation shifted over the 2 years. There was a sharp increase in the number of tweets from January 2020 to February 2020 due to the outbreak of COVID-19 in the United Kingdom. Then, the number of tweets gradually declined as of February 2020. Moreover, with identification of the COVID-19 Omicron variant in the United Kingdom in November 2021, the number of tweets grew again. Our findings reveal people’s attitudes and emotions toward topics related to COVID-19. For sentiment, approximately 60% of tweets were positive, 20% were neutral, and 20% were negative. For emotion, people tended to express highly positive emotions in the beginning of 2020, while expressing highly negative emotions over time toward the end of 2021. The topics also changed during the pandemic. Conclusions Through large-scale text mining of Twitter, our study found meaningful differences in public emotions and topics regarding the COVID-19 pandemic among different UK cities. Furthermore, efficient location-based and time-based comparative analysis can be used to track people’s thoughts and feelings, and to understand their behaviors. Based on our analysis, positive attitudes were common during the pandemic; optimism and anticipation were the dominant emotions. With the outbreak and epidemiological change, the government developed control measures and vaccination policies, and the topics also shifted over time. Overall, the proportion and expressions of emojis, sentiments, emotions, and topics varied geographically and temporally. Therefore, our approach of exploring public emotions and topics on the pandemic from Twitter can potentially lead to informing how public policies are received in a particular geographical area.

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Section: Introductionmentioning

confidence: 99%

Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis

Alhuzali¹,

Zhang²,

Ananiadou³

2022

J Med Internet Res

Self Cite

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“…One of the complex illnesses associated with varieties of clinical associations and socioeconomic status is mental illness and it's being associated with diverse textual clinical data, coupled with those from social media and interviews [16]. Natural language processing (NLP) is one of the methods used by researchers as one means to aid in early diagnosis and empower the medical healthcare system.…”

Section: Related Workmentioning

confidence: 99%

Predictive Analysis of Mental Health Conditions Using AdaBoost Algorithm

Ogunseye

Adenusi²,

Nwanakwaugwu

et al. 2022

paradigmplus

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The presented research responds to increased mental illness conditions worldwide and the need for efficient mental health care (MHC) through machine learning (ML) implementations. The datasets employed in this investigation belong to a Kaggle repository named "Mental Health Tech Survey." The surveys for the years 2014 and 2016 were downloaded and aggregated. The prediction results for bagging, stacking, LR, KNN, tree class, NN, RF, and Adaboost yielded 75.93%, 75.93%, 79.89%, 90.42%, 80.69%, 89.95%, 81.22%, and 81.75% respectively. The AdaBoost ML model performed data cleaning and prediction on the datasets, reaching an accuracy of 81.75%, which is good enough for decision-making. The results were further used with other ML models such as Random Forest (RF), K-Nearest Neighbor (KNN), bagging, and a few others, with reported accuracy ranging from 81.22 to 75.93 which is good enough for decision making. Out of all the models used for predicting mental health treatment outcomes, AdaBoost has the highest accuracy.

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“…Natural language processing was originally used to examine classical tasks, such as structuring grammar in books. However, it has expanded to interpreting human viewpoints, including mail, online content, reviews, social networking postings, and media articles ( Zhang et al, 2022 ). In many ways, NLP can process a variety of languages ( Islam et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

An optimized deep learning approach for suicide detection through Arabic tweets

Baghdadi

Malki

Balaha

et al. 2022

PeerJ Computer Science

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Many people worldwide suffer from mental illnesses such as major depressive disorder (MDD), which affect their thoughts, behavior, and quality of life. Suicide is regarded as the second leading cause of death among teenagers when treatment is not received. Twitter is a platform for expressing their emotions and thoughts about many subjects. Many studies, including this one, suggest using social media data to track depression and other mental illnesses. Even though Arabic is widely spoken and has a complex syntax, depressive detection methods have not been applied to the language. The Arabic tweets dataset should be scraped and annotated first. Then, a complete framework for categorizing tweet inputs into two classes (such as Normal or Suicide) is suggested in this study. The article also proposes an Arabic tweet preprocessing algorithm that contrasts lemmatization, stemming, and various lexical analysis methods. Experiments are conducted using Twitter data scraped from the Internet. Five different annotators have annotated the data. Performance metrics are reported on the suggested dataset using the latest Bidirectional Encoder Representations from Transformers (BERT) and Universal Sentence Encoder (USE) models. The measured performance metrics are balanced accuracy, specificity, F1-score, IoU, ROC, Youden Index, NPV, and weighted sum metric (WSM). Regarding USE models, the best-weighted sum metric (WSM) is 80.2%, and with regards to Arabic BERT models, the best WSM is 95.26%.

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Natural language processing applied to mental illness detection: a narrative review

Cited by 170 publications

References 201 publications

Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis

Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis

Predictive Analysis of Mental Health Conditions Using AdaBoost Algorithm

An optimized deep learning approach for suicide detection through Arabic tweets

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