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Hibrit Yaklaşıma Dayalı X (Twitter) Duygu Analizi: Çevrimiçi Yemek Siparişi Üzerine Bir Uygulama

Yıl 2025, Cilt: 18 Sayı: 2, 143 - 167, 30.04.2025

Yıldırım Güneş , Murat Arıkan

https://doi.org/10.17671/gazibtd.1616709

Öz

X (eski adıyla Twitter) gibi çevrimiçi platformlardaki kullanıcı görüşlerinin duygu analizi için, genellikle sözlük tabanlı yaklaşımlar ve makine öğrenmesi yöntemleri kullanılır. Son çalışmalar, bu yaklaşımların hibrit kullanımının model performansını iyileştirdiğini vurgulamaktadır. Bu çalışmada, yemek siparişi ile ilgili metinlerin duygu analizi için hibrit bir sınıflandırma modeli öneriyoruz. Ayrıca, metin sınıflandırmanın yüksek boyutluluk problemi için kelimeleri toplulaştırmaya dayalı bir özellik seçim yöntemi öneriyoruz. Bu alandaki temel sorunlar, ayırt edici özelliklere sahip kelime sayısının düşük olması, yemek siparişi ile ilgili cümlelerin yorumlanmasının karmaşıklığı, metin sınıflandırmanın alan bağımlılığıdır. Sınıflandırma algoritmalarının ve alan sözlüğü tabanlı bir yaklaşımın birlikte kullanılması, bu zorlukların üstesinden gelinmesine katkıda bulunacaktır. Bu amaçla, çevrimiçi kullanıcıların görüşlerinden elde edilen veriler kullanılarak, biri duygu analizi için diğeri ise temel sözlükler olarak adlandırılan ürün-hizmet sistemleri sınıflandırması için olmak üzere iki alana özgü sözlük geliştirilmiştir. Temel sözlükler, bu sözlüklerdeki kelimelerin gruplandırılması ve sözkonusu gruplardan grubu temsil edecek bir kelimenin seçilmesiyle, daha az sayıda kelime içeren ve güçlendirilmiş sözlük olarak adlandırılan yeni sözlüklere dönüştürülmüştür. Duygu analizi için hibrit yaklaşımla, altı sınıflandırma algoritması, üç terim ağırlıklandırma yöntemi ve sözlüklerin kombinasyonlarından oluşan 144 model oluşturulmuştur. Çalışmada, 1 Ocak - 31 Aralık 2020 tarih aralığında X’ten paylaşılmış, 21 039 ve 14 389 tweetten oluşan iki veri seti kullanılmıştır. Modeller eğitilmiş, ilk veri seti üzerinde test edilmiş ve bunların arasından en iyi model seçimi yapılmıştır. İkinci veri seti seçilen modellerle analiz edilmiş ve sektör için öneriler sunulmuştur.

Anahtar Kelimeler

X (Twitter) duygu analizi sözlük tabanlı sınıflandırma çevrimiçi yemek siparişi doğal dil işleme özellik seçimi

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X (Twitter) Sentiment Analysis Based on Hybrid Approach: An Application for Online Food Ordering

Yıl 2025, Cilt: 18 Sayı: 2, 143 - 167, 30.04.2025

Yıldırım Güneş , Murat Arıkan

https://doi.org/10.17671/gazibtd.1616709

Öz

For sentiment analysis of user opinions on online platforms such as X (formerly known as Twitter), dictionary-based approaches and machine learning methods are generally used. Recent studies emphasize that hybridizing these approaches improves model performance. In this study, we propose a hybrid classification model for sentiment analysis of texts on food ordering. In addition, we suggest a feature selection method based on aggregating words for the high-dimensionality problem of text classification. The main problems in that domain are low number of words with distinctive features, complexity of interpretation of food ordering field, domain dependency of text classification. The use of classification algorithms and a domain lexicon-based approach will contribute to overcoming these difficulties. For this purpose, two domain-specific lexicons are developed using data from online users' opinions, one for sentiment analysis and the other for product-service systems classification, referred to as basic lexicons. Basic lexicons have been transformed into new lexicons with fewer words, referred to as boosted lexicons, by grouping the words in basic lexicons and representing the groups with a single word in boosted lexicons. 144 models of combinations of six classification algorithms, three term weighting methods, and the lexicons are created in a hybrid approach for sentiment analysis. The study used two datasets of 21 039 and 14 389 tweets obtained from X between January 1 and December 31, 2020. The models were trained, tested on the first dataset, and the best models were selected. The second dataset is analyzed with the selected models, we present proposals for the industry.

Anahtar Kelimeler

X (twitter) sentiment analysis lexicon-based classification online food order natural language process feature selection

Kaynakça

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  • M. Wankhade, A.C.S. Rao and C. Kulkarni, “A survey on sentiment analysis methods, applications, and challenges”, Artificial Intelligence Review, (55), 5731–5780. 2022.
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  • J.R. Chang, H.Y. Liang and L.S. Chen, “Novel feature selection approaches for improving the performance of sentiment classification”, Journal of Ambient Intelligence and Humanized Computing, 2020.
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  • G. Yoo and J. Nam, “A Hybrid Approach to Sentiment Analysis Enhanced by Sentiment Lexicons and Polarity Shifting Devices”, The 13th Workshop on Asian Language Resources, Kiyoaki Shirai, Miyazaki, Japan, May 2018.
  • B. Erşahin, Ö. Aktaş and D. Kılınç, et al. "A hybrid sentiment analysis method for Turkish", Turkish Journal of Electrical Engineering and Computer Sciences, 27(3), 1780 -1793, 2019.
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  • M.R.R. Rana, S.U. Rehman and A. Nawaz, et al, “Aspect-based sentiment analysis for social multimedia: A hybrid computational framework”, Computer Systems and Engineering, 46(2), 2415-2428, 2023.
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  • . Barreto, R. Moura and J. Carvalho, et al, “Sentiment analysis in tweets: an assessment study from classical to modern word representation models”, Data Mining and Knowledge Discovery, (37), 318–380, 2023.
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  • K. Ahmed, M.I. Nadeem and Z. Zheng, et al, “Breaking down linguistic complexities: A structured approach to aspect-based sentiment analysis”, Journal of King Saud University - Computer and Information Sciences, 35 (8), 1-21, 2023.
  • Ö. Aktaş, B. Coskuner and I. Soner, “Turkish Sentiment Analysis Using Machine Learning Methods: Application on Online Food Order Site Reviews”, Journal of Artificial Intelligence and Data Science, 1(1), 1-10, 2021.
  • Z. Liu, C. Yang and S. Rüdian, et al, “Temporal emotion- aspect modeling for discovering what students are concerned about in online course forums”, Interactive Learning Environment, 27 (5–6), 598–627, 2019.
  • Imbalanced Data. https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data. 25. 04. 2023.
  • V. A. Pitogo and C. D. L. Ramos, “Social media enabled e-participation: a lexicon-based sentiment analysis using unsupervised machine learning”, In: Proceedings of the 13th International Conference on Theory and Practice of Electronic Governance, ACM, 518–528, 2020.
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  • Y. Kim, T. Y. Choi, T. Yan and K. Dooley, “Structural investigation of supply networks: A social network analysis approach”, Journal of Operations Management, 29, 194-211, 2011.
  • Zargan Translation Dictionary. https://www.zargan.com/tr/q/ demeaning-ceviri-nedir, 2021.
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  • O. C. Atalaya, J. A. Tuesta, D. B. Mares, A. G. Pacheco, O. M. León, M. Q. Silvestre, G. T. Quispe and R. S. Bazalar, “K-Fold Cross-Validation through Identification of the Opinion Classification Algorithm for the Satisfaction of University Students”, International Journal of Online and Biomedical Engineering (iJOE). 19(11), 140-158, 2023.
  • A. Srivastava. “A Review on Sentiment Analysis of Twitter Data Using Machine Learning Techniques”. International Journal of Engineering and Management Research, 14(1), 2024.
Toplam 127 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Makine Öğrenme (Diğer), Veri Madenciliği ve Bilgi Keşfi, Doğal Dil İşleme
Bölüm Makaleler
Yazarlar

Yıldırım Güneş 0000-0001-6543-6399

Murat Arıkan 0000-0003-1437-8939

Yayımlanma Tarihi 30 Nisan 2025
Gönderilme Tarihi 9 Ocak 2025
Kabul Tarihi 27 Şubat 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 18 Sayı: 2

Kaynak Göster

APA Güneş, Y., & Arıkan, M. (2025). X (Twitter) Sentiment Analysis Based on Hybrid Approach: An Application for Online Food Ordering. Bilişim Teknolojileri Dergisi, 18(2), 143-167. https://doi.org/10.17671/gazibtd.1616709
 Kapak Resmi İndir