Facebook Social Media for Depression Detection in the Thai Community

Katchapakirin, Kantinee; Wongpatikaseree, Konlakorn; Yomaboot, Panida; Kaewpitakkun, Yongyos

doi:10.1109/jcsse.2018.8457362

Cited by 54 publications

(34 citation statements)

References 3 publications

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“…Supervised learning [26]. Algorithms with labelled [18,21,22] data and defines the variables they want the system to assess for correlations [8]. The input and output of the entire learning algorithm or system is specified.…”

Section: Papersmentioning

confidence: 99%

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

2021

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Section: Papersmentioning

confidence: 99%

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

2021

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“…The posts and comments consist of people's opinions, so they have specified that the mood and behaviour pattern can analyze users' thoughts. The research of Facebook Social Media for Depression Detection in the Thai Community [14] has discussed the importance of validating the users' engagement with Facebook and how they have used Facebook to share their thoughts. They have also suggested that social media can detect people's stress at a specific time.…”

Section: Literature Reviewmentioning

confidence: 99%

Music Recommendation System based on Emotions in User’s Social Media behaviour

Perera¹

2021

Preprint

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The modern lifestyle makes people more competitive. It can lead to more stressful situations in our lifestyle. With the changes in human emotional behaviour, they tend to share their feelings on social media platforms rather than communicating with relatives. Studies proved that people used to listen to music to avoid emotional situations in their life. But there is no proper way to get the most accurate music to listen to and avoid emotional conflicts. Resolving these conflicts, the music recommendation system based on emotion introduced. It analyses the users' recent social media content and detects the various kind of emotions. To ensure that the suggested music is relevant to users emotions, the lyrics analysing was done using natural language processing techniques to identify the music emotions. Most people pay attention to the meaning of the songs, that was the major reason to consider the emotions of the lyrics.

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“…Therefore, detecting depression from daily living habits can solve the lack of existing methods. Online behaviour is considered to be an excellent source to describe the emotional state [7].…”

Section: Depression Detectionmentioning

confidence: 99%

Mental Disorder Detection via Social Media Mining using Deep Learning

Kholifah

Syarif

Badriyah

2020

KINETIK

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Mental disorders are a disease that cannot be physically seen, so diagnosing someone with a mental disorder is not easy. Due to the imperceptible nature of mental disorders, diagnosing a patient with a mental disorder is a challenging task. Therefore, detection in people with mental disorders can be done by looking at the symptoms they experience. One symptom in patients with mental disorders is solitude. Patients with mental disorders feel indifferent to their environment and focus on what is happening to them. Therefore, the patient looks for a place that can accommodate his feelings. Twitter is one of the most widely used media in measuring one's personality through everyday statements. The symptoms as suggested by psychologists can be explored more broadly using Natural Languages Processing. In this case, it takes a lexicon containing keywords that can show symptoms of depression. This study uses five criteria as a measure of mental health in a statement: sentiment, basic emotions, the use of personal pronouns, absolutist words, and negative words. The results show that the use of sentiments, emotions, and negative words in a statement is very influential in determining the level of depression. A depressed person more often uses negative words that indicate his self-despair, prolonged sadness, even suicidal thoughts (e.g. "sadly”, “scared”, “die”, “suicide”). In the classification process, using LSTM Deep Learning obtained an accuracy of 70.89%; precision of 50.24%; recall 70.89%.

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Facebook Social Media for Depression Detection in the Thai Community

Cited by 54 publications

References 3 publications

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

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

Music Recommendation System based on Emotions in User’s Social Media behaviour

Mental Disorder Detection via Social Media Mining using Deep Learning

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