DETECTING INDICATORS OF MENTAL DISORDER IN SOCIAL MEDIA POSTS VIA HYBRID DEEP LEARNING AND NATURAL LANGUAGE PROCESSING / (Kayıt no. 292975)
[ düz görünüm ]
| 000 -BAŞLIK | |
|---|---|
| Sabit Uzunluktaki Kontrol Alanı | 02938nam a22002657a 4500 |
| 003 - KONTROL NUMARASI KİMLİĞİ | |
| Kontrol Alanı | KOHA |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI | |
| Kontrol Alanı | 20250108101216.0 |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ | |
| Sabit Alan | 240927d2024 cy de||| |||| 00| 0 eng d |
| 040 ## - KATALOGLAMA KAYNAĞI | |
| Özgün Kataloglama Kurumu | CY-NiCIU |
| Kataloglama Dili | eng |
| Çeviri Kurumu | CY-NiCIU |
| Açıklama Kuralları | rda |
| 041 ## - DİL KODU | |
| Metin ya da ses kaydının dil kodu | eng |
| 090 ## - Yerel Tasnif No | |
| tasnif no | YL 3516 |
| Cutter no | A33 2024 |
| 100 1# - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Ahadi, Seyedeh Aridis |
| 245 10 - ESER ADI BİLDİRİMİ | |
| Başlık | DETECTING INDICATORS OF MENTAL DISORDER IN SOCIAL MEDIA POSTS VIA HYBRID DEEP LEARNING AND NATURAL LANGUAGE PROCESSING / |
| Sorumluluk Bildirimi | SEYEDEH ARIDIS AHADI ; SUPERVISOR, ASST. PROF. DR. KIAN JAZAYERI |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024 |
| 300 ## - FİZİKSEL TANIMLAMA | |
| Sayfa, Cilt vb. | 134 sheets ; |
| Boyutları | 30 cm |
| Birlikteki Materyal | +1 CD ROM |
| 336 ## - CONTENT TYPE | |
| Source | rdacontent |
| Content type term | text |
| Content type code | txt |
| 337 ## - MEDIA TYPE | |
| Source | rdamedia |
| Media type term | unmediated |
| Media type code | n |
| 338 ## - CARRIER TYPE | |
| Source | rdacarrier |
| Carrier type term | volume |
| Carrier type code | nc |
| 502 ## - TEZ NOTU | |
| Tez Notu | Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Information Technologies |
| 520 ## - ÖZET NOTU | |
| Özet notu | Identifying people who are suicidal on social media has become more crucial recently. Systems that predict people's mental health can be developed using the information offered by the textual data. In recent times, individuals have turned to social media platforms to share their experiences and seek mental health support. This trend has spurred researchers to utilize the data, applying various Natural Language Processing (NLP) and Machine Learning (ML) techniques to assist those in need. In this study, a framework to identify suicidal thoughts in social media using a combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) classifier model is proposed. Different combinations of embedding, activation functions, and solver algorithms are implemented on this network, and then the results are compared. The used methods contain Count and TF-IDF as word embedding, RReLU, Tanh, Mish, and ELU as activation functions, and Adam, Adamax, Adadelta, Adagrad, and RMSprop as solver algorithms. Bagging and stacking classifiers have been utilized on the network to create an ensemble of the basic models. In total, 82 different methods have been applied. The main dataset is gathered from four Reddit mental health datasets on suicidality. Overall, 59996 user texts from 2018 to 2020 were retrieved to be analyzed. Results are compared based on 5 performance metrics (accuracy, F1 score, precision, recall, and AUC) and elapsed times. The output accuracy scores are in the range of 74% - 86%. The combination of TF_IDF, RReLU, and Adam achieved the overall top performance. As a result, it is evident that real-world suicidality detection using ML is invaluable. ML algorithms can analyze social media posts, text messages, and behavioral patterns to provide insights and cautions. This approach can provide a proper platform so that suicidality and mental illness do not threaten the fabric of our society anymore. |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Information Technologies |
| Alt başlık biçimi | Dissertations, Academic |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Jazayeri, Kian |
| İlişkili Terim | supervısor |
| 942 ## - EK GİRİŞ ÖGELERİ (KOHA) | |
| Sınıflama Kaynağı | Dewey Onlu Sınıflama Sistemi |
| Materyal Türü | Thesis |
| Geri Çekilme Durumu | Kayıp Durumu | Sınıflandırma Kaynağı | Kredi için değil | Koleksiyon Kodu | Kalıcı Konum | Mevcut Konum | Raf Yeri | Kayıt Tarih | Source of acquisition | Toplam Ödünçverme | Yer Numarası | Demirbaş Numarası | Son Görülme Tarihi | Kopya Bilgisi | Fatura Tarihi | Materyal Türü | Genel / Bağış Notu |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Depo | 23.10.2024 | Bağış | YL 3516 A33 2024 | T3963 | 23.10.2024 | C.1 | 23.10.2024 | Thesis | Information Technologies | ||||
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Görsel İşitsel | 23.10.2024 | Bağış | YL 3516 A33 2024 | CDT3963 | 23.10.2024 | C.1 | 23.10.2024 | Suppl. CD | Information Technologies |