Combining Sentiment Analysis Models Using Stacking Ensemble Learning Techniques on BIST30 Stocks

Mahmut Sami Sivri

doi:10.5281/zenodo.13996517

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Combining Sentiment Analysis Models Using Stacking Ensemble Learning Techniques on BIST30 Stocks

Öz

In recent years, sentiment analysis has become a crucial task in the field of natural language processing (NLP). Despite significant advancements in individual sentiment analysis models, combining multiple models can further enhance performance and robustness. This paper proposes an ensemble model using stacking to integrate the outputs of different sentiment analysis models applied to news articles related to BIST30 stocks traded on Borsa Istanbul. The base models include Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), Naive Bayes, and Support Vector Machines (SVM). The meta-learner is a logistic regression model that aggregates the predictions of the base models. This ensemble approach demonstrates improved accuracy and generalization capabilities over single-model approaches in analyzing the sentiment of financial news.

Anahtar Kelimeler

Sentiment Analysis, Ensemble Learning, Stacking, Machine Learning, Stock Markets

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Endüstri Mühendisliği

Bölüm

Araştırma Makalesi

Yazarlar

Mahmut Sami Sivri ^*
0000-0002-9391-1801
Türkiye

Yayımlanma Tarihi

27 Ekim 2024

Gönderilme Tarihi

6 Ağustos 2024

Kabul Tarihi

22 Ekim 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 1 Sayı: 2

DOI

https://doi.org/10.5281/zenodo.13996517

IZ

https://izlik.org/JA73YM69SR

APA

Sivri, M. S. (2024). Combining Sentiment Analysis Models Using Stacking Ensemble Learning Techniques on BIST30 Stocks. Hendese Teknik Bilimler ve Mühendislik Dergisi, 1(2), 91-97. https://doi.org/10.5281/zenodo.13996517

AMA

1.Sivri MS. Combining Sentiment Analysis Models Using Stacking Ensemble Learning Techniques on BIST30 Stocks. HENDESE. 2024;1(2):91-97. doi:10.5281/zenodo.13996517

Chicago

Sivri, Mahmut Sami. 2024. “Combining Sentiment Analysis Models Using Stacking Ensemble Learning Techniques on BIST30 Stocks”. Hendese Teknik Bilimler ve Mühendislik Dergisi 1 (2): 91-97. https://doi.org/10.5281/zenodo.13996517.

EndNote

Sivri MS (01 Ekim 2024) Combining Sentiment Analysis Models Using Stacking Ensemble Learning Techniques on BIST30 Stocks. Hendese Teknik Bilimler ve Mühendislik Dergisi 1 2 91–97.

IEEE

[1]M. S. Sivri, “Combining Sentiment Analysis Models Using Stacking Ensemble Learning Techniques on BIST30 Stocks”, HENDESE, c. 1, sy 2, ss. 91–97, Eki. 2024, doi: 10.5281/zenodo.13996517.

ISNAD

Sivri, Mahmut Sami. “Combining Sentiment Analysis Models Using Stacking Ensemble Learning Techniques on BIST30 Stocks”. Hendese Teknik Bilimler ve Mühendislik Dergisi 1/2 (01 Ekim 2024): 91-97. https://doi.org/10.5281/zenodo.13996517.

JAMA

1.Sivri MS. Combining Sentiment Analysis Models Using Stacking Ensemble Learning Techniques on BIST30 Stocks. HENDESE. 2024;1:91–97.

MLA

Sivri, Mahmut Sami. “Combining Sentiment Analysis Models Using Stacking Ensemble Learning Techniques on BIST30 Stocks”. Hendese Teknik Bilimler ve Mühendislik Dergisi, c. 1, sy 2, Ekim 2024, ss. 91-97, doi:10.5281/zenodo.13996517.

Vancouver

1.Mahmut Sami Sivri. Combining Sentiment Analysis Models Using Stacking Ensemble Learning Techniques on BIST30 Stocks. HENDESE. 01 Ekim 2024;1(2):91-7. doi:10.5281/zenodo.13996517

Combining Sentiment Analysis Models Using Stacking Ensemble Learning Techniques on BIST30 Stocks

BIST30 Hisseleri Üzerinde Duygu Analizi Modellerini Yığınlama Topluluk Öğrenmesi Teknikleriyle Birleştirme

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Anahtar Kelimeler

Combining Sentiment Analysis Models Using Stacking Ensemble Learning Techniques on BIST30 Stocks

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

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Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

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