CLUDS: COMBINING LABELED AND UNLABELED DATA WITH LOGISTIC REGRESSION FOR SOCIAL MEDIA ANALYSIS

Öz

Automatic text classification and sentiment polarity detection are two important research problems of social media analysis. The meanings of the words are so important that they need to be captured by a document classification algorithm to reach an accurate classification performance. Another important issue with the text classification is the scarcity of labeled data. In this study, Combining Labeled and Unlabeled Data with Semantic Values of Terms (CLUDS) is presented. CLUDS has the following steps: preprocessing, instance labeling, combining labeled and unlabeled data, and prediction. In preprocessing step Latent Dirichlet Allocation (LDA) algorithm is used. In instance labeling step Logistic Regression is applied. In CLUDS, relevance values computation has been applied as a supervised term weighting methodology in the text classification field. Still, according to the literature, CLUDS is the first attempt that uses both relevance and weighting calculation in a semi-supervised semantic kernel for Support Vector Machines (SVM). In this study, Sprinkled-CLUDS and Adaptive-Sprinkled-CLUDS have also been implemented. Evaluated experimental results show that CLUDS, Sprinkled-CLUDS and Adaptive-Sprinkled-CLUDS generate a valuable performance gain over the baseline algorithms on test sets.

Anahtar Kelimeler

• Tweet Classification
• Latent Dirichlet Allocation
• Logistic Regression
• Social Media Analysis
• Sentiment Polarity Detection

CLUDS: SOSYAL MEDYA ANALİZİ İÇİN ETİKETLİ VE ETİKETSİZ VERİLERİ LOJİSTİK REGRESYON İLE BİRLEŞTİRME

Öz

Otomatik metin sınıflandırması ve duygu polarite tespiti, sosyal medya analizinin iki önemli araştırma problemidir. Kelimelerin anlamları o kadar önemlidir ki, doğru bir sınıflandırma performansına ulaşmak için bir belge sınıflandırma algoritması tarafından yakalanmaları gerekir. Metin sınıflandırmasıyla ilgili bir diğer önemli konu, etiketlenmiş verilerin azlığıdır. Bu çalışmada, yeni bir yarı denetimli metodoloji sunulmuştur. Etiketli ve Etiketlenmemiş Verilerin Anlamsal Terim Değerleri (CLUDS) ile Birleştirilmesi olarak adlandırılır. CLUDS şu adımlara sahiptir: ön işleme, örnek etiketleme, etiketli ve etiketlenmemiş verileri birleştirme ve tahmin. Ön işleme adımında Latent Dirichlet Allocation (LDA) algoritması kullanılmaktadır. Örnek etiketleme adımında Lojistik Regresyon uygulanır. CLUDS'ta, alaka değerleri hesaplaması, metin sınıflandırma alanında denetimli bir terim ağırlıklandırma yöntemi olarak uygulanmıştır. Literatüre göre, CLUDS, Destek Vektör Makineleri (SVM) için yarı denetimli bir semantik çekirdekte hem alaka düzeyi hem de ağırlık hesaplamasını kullanan ilk girişimdir. Bu çalışmada, Sprinkled-CLUDS ve Adaptive-Sprinkled-CLUDS da uygulanmıştır. Değerlendirilen deney sonuçları CLUDS, Sprinkled-CLUDS ve Adaptive-Sprinkled-CLUDS'ın test setlerinde temel algoritmalara göre değerli bir performans kazancı sağladığını göstermektedir.

Anahtar Kelimeler

• Tweet Sınıflandırması
• Gizli Dirichlet Analizi
• Lojistik Regresyon
• Sosyal Medya Analizi
• Duygu Polarite Tespiti

Kaynakça

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