IDENTIFYING HARMFUL TWEETS: COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS / (Kayıt no. 293078)
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| 000 -BAŞLIK | |
|---|---|
| Sabit Uzunluktaki Kontrol Alanı | 03086nam a22002657a 4500 |
| 003 - KONTROL NUMARASI KİMLİĞİ | |
| Kontrol Alanı | KOHA |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI | |
| Kontrol Alanı | 20250110130645.0 |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ | |
| Sabit Alan | 240927d2024 cy e|||| |||| 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 3559 |
| Cutter no | K35 2024 |
| 100 1# - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Al-Kaladi, Bandar Khaled Mohammed |
| 245 10 - ESER ADI BİLDİRİMİ | |
| Başlık | IDENTIFYING HARMFUL TWEETS: COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS / |
| Sorumluluk Bildirimi | BANDAR KHALED MOHAMMED AL-KALADI ; 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. | 46 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 | Sentiment analysis on platforms along the lines of Twitter provides actual perception into public opinions and trends. This study evaluated the performance of one deep learning model, the Gated Recurrent Unit, and three machine learning algorithms, Support Vector Machine, Random Forest, and Naive Bayes, in differentiating between normal and harmful tweets. The dataset, sourced from Twitter, goes through preprocessing in order to remove noise, including user mentions, hashtags, links, and stop words. The cleaned dataset was split into training and testing sets in order to train each classifier. Performance metrics such as accuracy, precision, recall, F1-score, and confusion matrices were used in order to evaluate each model. An accuracy of 92.02% was accomplished by Naive Bayes, with normal tweets possessing a precision of 0.96 and recall of 0.88, and harmful tweets possessing a precision of 0.89 and recall of 0.96. Random Forest accomplished the highest accuracy at 97.28%, with normal tweets possessing a precision of 0.98 and recall of 0.97, and harmful tweets possessing a precision of 0.97 and recall of 0.98. Support Vector Machine (SVM) accomplished an accuracy of 96.92%, with normal tweets having a precision of 0.96 and recall of 0.98, and harmful tweets possessing a precision of 0.98 and recall of 0.96. The Gated Recurrent Unit (GRU) achieved an accuracy of 95.02%, with normal tweets having a precision of 0.98 and recall of 0.92, and harmful tweets possessing a precision of 0.92 and recall of 0.98. Each algorithm had advantages and disadvantages: Naive Bayes expressed high precision but lower recall for harmful tweets. Random Forest displayed balanced precision and recall. SVM achieved high accuracy with strong performance in both precision and recall. And GRU successfully handled sequential patterns. The findings highlight the relative advantages and limitations of deep learning and machine learning perspectives in Twitter sentiment analysis, with Random Forest and Support Vector Machine appearing as the majority of its effective among the evaluated methods. |
| 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 | 19.11.2024 | Bağış | YL 3559 K35 2024 | T4006 | 19.11.2024 | C.1 | 19.11.2024 | Thesis | Information Technologies | ||||
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Görsel İşitsel | 19.11.2024 | Bağış | YL 3559 K35 2024 | CDT4006 | 19.11.2024 | C.1 | 19.11.2024 | Suppl. CD | Information Technologies |