Detecting the magnitude of depression in Twitter users using sentiment analysis

Stephen, Jini Jojo; Prabu, P.

doi:10.11591/ijece.v9i4.pp3247-3255

Cited by 47 publications

(12 citation statements)

References 5 publications

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“…The way of calculation includes using VADER sentiment analysis (Valence Aware Dictionary and sEntiment Reasoner) 207 , SenticNet 5 lexicon 208 , AFINN lexicon 209 . 63 , 210 – 212 Emotion scores The emotion scores indicates the user’s emotions and opinions of texts to an extent, which is beneficial for mental issues detection. NRC Affect Intensity Lexicon 213 are always used.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2022

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Mental illness is highly prevalent nowadays, constituting a major cause of distress in people’s life with impact on society’s health and well-being. Mental illness is a complex multi-factorial disease associated with individual risk factors and a variety of socioeconomic, clinical associations. In order to capture these complex associations expressed in a wide variety of textual data, including social media posts, interviews, and clinical notes, natural language processing (NLP) methods demonstrate promising improvements to empower proactive mental healthcare and assist early diagnosis. We provide a narrative review of mental illness detection using NLP in the past decade, to understand methods, trends, challenges and future directions. A total of 399 studies from 10,467 records were included. The review reveals that there is an upward trend in mental illness detection NLP research. Deep learning methods receive more attention and perform better than traditional machine learning methods. We also provide some recommendations for future studies, including the development of novel detection methods, deep learning paradigms and interpretable models.

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Section: Resultsmentioning

confidence: 99%

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

et al. 2022

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“…Stephen et al [12] collected their dataset from users who identified as depressed. To reach more users, they implemented specific keywords used by those who identified as depressed.…”

Section: Related Literaturementioning

confidence: 99%

Twitter Arabic Sentiment Analysis to Detect Depression Using Machine Learning

Musleh¹,

Alkhales²,

AlMakki³

et al. 2022

Computers, Materials &Amp; Continua

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Depression has been a major global concern for a long time, with the disease affecting aspects of many people's daily lives, such as their moods, eating habits, and social interactions. In Arabic culture, there is a lack of awareness regarding the importance of facing and curing mental health diseases. However, people all over the world, including Arab citizens, tend to express their feelings openly on social media, especially Twitter, as it is a platform designed to enable the expression of emotions through short texts, pictures, or videos. Users are inclined to treat their Twitter accounts as diaries because the platform affords them anonymity. Many published studies have detected the occurrence of depression among Twitter users on the basis of data on tweets posted in English, but research on Arabic tweets is lacking. The aim of the present work was to develop a model for analyzing Arabic users' tweets and detecting depression among Arabic Twitter users. And expand the diversity of user tweets, by adding a new label ("neutral") so the dataset include three classes ("depressed", "non-depressed", "neutral"). The model was created using machine learning classifiers and natural language processing techniques, such as Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), K-nearest Neighbors (KNN), AdaBoost, and Naïve Bayes (NB). The results showed that the RF classifier outperformed the others, registering an accuracy of 82.39%.

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“…The PHQ-9 test results showed that 120 participants are depression-negative, and 40 participants are depression-positive. The Facebook data were collected for 14…”

Section: Facebook Data Collectionmentioning

confidence: 99%

“…The first is works that analyze the posted texts to detect and learn keywords related to depression symptoms such as worthless, lonely, suicidal thoughts, feel sad, and anxiety feelings [10], [11]. The second is to use a machine-learning technique to create a classification model for depression users from annotated social network text data [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

Proactive depression detection from Facebook text and behavior data

Hemtanon

Aekwarangkoon²,

Kittiphattanabawon³

2022

IJECE

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<span lang="EN-US">This paper proposes a proactive method to detect the clinical depression affected person from post and behavior data from Facebook, called text-based and behavior-based models, respectively. For a text-based model, the words that make up the posts are separated and converted into vectors of terms. A machine learning classification applies the term frequency- inverse document frequency technique to identify important or rare words in the posts. For the behavior-based model, the statistical values of the behavioral data were designed to capture depressive symptoms. The results showed that the behavior-based model was able to detect depressive symptoms better than the text-based model. Regarding performance, a detection model using behaviors yields significantly higher F1 scores than those using words in the post. The K-nearest neighbor (</span><span lang="EN-US">KNN) classifier is the best model with the highest F1 score of 1.0, while the highest F1 score of the behavior-based model is 0.88. An analysis of the predominant features influencing depression signifies that posted messages could detect feelings of self-hatred and suicidal thoughts. At the same time, behavioral manifestations identified depressed people who manifested as restlessness, insomnia, decreased concentration.</span>

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Detecting the magnitude of depression in Twitter users using sentiment analysis

Cited by 47 publications

References 5 publications

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

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

Twitter Arabic Sentiment Analysis to Detect Depression Using Machine Learning

Proactive depression detection from Facebook text and behavior data

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