Ensemble of Classifiers and Term Weighting Schemes for Sentiment Analysis in Turkish

Abstract

With the advancement of information and communication technology, social networking and microblogging sites have become a vital source of information. Individuals can express their opinions, grievances, feelings, and attitudes about a variety of topics. Through microblogging platforms, they can express their opinions on current events and products. Sentiment analysis is a significant area of research in natural language processing because it aims to define the orientation of the sentiment contained in source materials. Twitter is one of the most popular microblogging sites on the internet, with millions of users daily publishing over one hundred million text messages (referred to as tweets). Choosing an appropriate term representation scheme for short text messages is critical. Term weighting schemes are critical representation schemes for text documents in the vector space model. We present a comprehensive analysis of Turkish sentiment analysis using nine supervised and unsupervised term weighting schemes in this paper. The predictive efficiency of term weighting schemes is investigated using four supervised learning algorithms (Naive Bayes, support vector machines, the k-nearest neighbor algorithm, and logistic regression) and three ensemble learning methods (AdaBoost, Bagging, and Random Subspace). The empirical evidence suggests that supervised term weighting models can outperform unsupervised term weighting models.

An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding

“…Other works have been conducted on fairness for text classification tasks. Some researchers [ 15 ] analyzed different sentiment analysis techniques on the Turkish language with supervised and unsupervised ensemble models to explore the predictive efficiency of the term weighting schemes which is a process to compute and assign a numeric value to each term. The results indicated that supervised term weighting models can outperform unsupervised models in term weighting.…”

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“…e Naive Bayes classifier is commonly used in many applications like sentiment classifications and in different ensemble prediction models [16][17][18].…”

Türkçe Metin Madenciliği için Dikkat Mekanizması Tabanlı Derin Öğrenme Mimarilerinin Değerlendirilmesi

“…Deneysel analizde, Türkçe Twitter mesajları içeren, terim ağırlıklandırma yöntemlerinin duygu analizi alanındaki etkinliklerini değerlendirmek amacıyla oluşturulmuş bir veri seti kullanılmıştır [22]. Veri seti, 10,500'ü olumlu ve 10,500'ü olumsuz olmak üzere 21000 Twitter mesajı içermektedir.…”

“…Derlem için, açıklayıcılar arasında mükemmel bir uyum olduğunu gösteren 0.82'lik bir puan elde edilmiştir. Veri seti üzerinde [22]'de belirtilen önişleme adımları uygulanarak veri seti, derin öğrenme modelleri ile işlemeye hazır hale getirilmiştir. Derin öğrenme mimarilerinin performansını değerlendirmek amacıyla, doğru sınıflandırma oranı, F-ölçütü, geri çağırma ve hassasiyet ölçütleri kullanılmıştır.…”