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Journals / Doğuş Üniversitesi Dergisi / 2026 / Cilt: 27 Sayı: 1

STOCK SENTIMENT ANALYSIS FROM NEWS HEADLINES BY USING ENSEMBLE BOOSTING ALGORITHMS

HABER BAŞLIKLARI KULLANILARAK HİSSE SENEDİ DUYGU ANALİZİ: TOPLULUK GÜÇLENDİRME ALGORİTMALARIYLA BİR YAKLAŞIM

Aslı Örgerim(BURDUR MEHMET AKİF ERSOY ÜNİVERSİTESİ, BUCAK ZELİHA TOLUNAY UYGULAMALI TEKNOLOJİ VE İŞLETMECİLİK YÜKSEKOKULU, YÖNETİM BİLİŞİM SİSTEMLERİ BÖLÜMÜ)0000-0001-7785-6200
Adnan Kalkan(BURDUR MEHMET AKİF ERSOY ÜNİVERSİTESİ, BUCAK ZELİHA TOLUNAY UYGULAMALI TEKNOLOJİ VE İŞLETMECİLİK YÜKSEKOKULU, YÖNETİM BİLİŞİM SİSTEMLERİ BÖLÜMÜ)0000-0002-2270-4100
Pages
538–550
Publication Date
2026-02-26
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results demonstrate that the proposed models achieved successful performance in sentiment classification.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Stock sentiment analysis using machine learning techniques is an active area of research and development.
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Key Findings & Data:The dataset used consists of 3,897 unique news headlines from various companies.
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Conclusion & Impact:The results demonstrate that the proposed models achieved successful performance in sentiment classification.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Hisse Senedi Duygu Analizi · Topluluk Öğrenme · Topluluk Güçlendirme

Abstract

Stock sentiment analysis using machine learning techniques is an active area of research and development. Through these techniques, it is possible to analyze whether news headlines are likely to have a positive or negative sentiment, which is considered a potential factor influencing stock price movements. The analysis in this study is based on factors such as company-related news headlines and their publication dates. This study aims to introduce a stock sentiment analysis methodology based on Ensemble Boosting Algorithms. The dataset used consists of 3,897 unique news headlines from various companies. In addition to Ensemble Boosting Algorithms, widely used classification algorithms were also trained and compared. The results demonstrate that the proposed models achieved successful performance in sentiment classification.

Özet

Hisse senedi duygu analizi, makine öğrenmesi teknikleri kullanılarak yürütülen aktif bir araştırma ve geliştirme alanıdır. Bu teknikler aracılığıyla, haber başlıklarının olumlu veya olumsuz bir duyguya sahip olma olasılığı analiz edilebilmekte ve bu durum, hisse senedi fiyat hareketlerini etkileyebilecek potansiyel bir faktör olarak değerlendirilmektedir. Bu çalışmada, şirketlerle ilgili haber başlıkları ve bu başlıkların yayın tarihleri dikkate alınarak bir duygu analizi yöntemi önerilmiştir. Çalışmada, Ensemble Boosting algoritmalarına dayalı bir hisse senedi duygu analizi metodolojisi geliştirilmiştir. Kullanılan veri seti, çeşitli şirketlere ait toplam 3.897 benzersiz haber başlığından oluşmaktadır. Ensemble Boosting algoritmalarına ek olarak, literatürde yaygın olarak kullanılan diğer sınıflandırma algoritmaları da eğitilmiş ve karşılaştırılmıştır. Elde edilen sonuçlar, önerilen modellerin haber başlıklarının duygu sınıflandırmasında başarılı performans sergilediğini göstermektedir.

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Cite This Article

Örgerim, A., Kalkan, A. (2026). STOCK SENTIMENT ANALYSIS FROM NEWS HEADLINES BY USING ENSEMBLE BOOSTING ALGORITHMS. Doğuş Üniversitesi Dergisi, Cilt: 27 Sayı: 1, 538–550. https://doi.org/10.31671/doujournal.1695398

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