Twitter duygu analizinde terim ağırlıklandırma yönteminin etkisi

Abstract

Terim ağırlıklandırma, metin sınıflandırmada sonuçlar üzerinde doğrudan etkili olan önemli bir adımdır. Ancak, bir metin sınıflandırma problemi olarak ele alınan duygu analizinde farklı önişleme tekniklerine bağlı olarak ağırlıklandırma yönteminin davranışı değişebilmektedir. Bu çalışmada bilgi getirimi, metin sınıflandırma, doküman filtreleme gibi farklı çalışma alanları için yakın zamanda önerilen yöntemler Twitter duygu analizinde uygulanmış ve sonuçlar üzerindeki etkisi incelenmiştir. Öznitelikler çıkarılırken kelime torbası (BoW) ve karakter seviye N-gram olmak üzere iki farklı model kullanılmıştır. Deneyler Türkçe ve İngilizce Twitter mesajlarından oluşan veri kümeleri üzerinde uygulanmıştır. Twitter mesajlarının duygu sınıflandırması, Gizli Dirichlet Ataması (LDA) tabanlı konu modeli ile gerçekleştirilmiştir. Sınıflandırma aşamasında ise Destek Vektör Makinesi (SVM) algoritması kullanılmıştır. Deneysel sonuçlara göre, Twitter duygu analizi çalışmalarında kullanılabilecek en etkili terim ağırlıklandırma yöntemi önerilmiştir.

Keywords

• Twitter,Duygu analizi,Terim ağırlıklandırma

The impact of term weighting method on Twitter sentiment analysis

Abstract

Term weighting is an important step which has direct impact on the result in classical text classification. However, the behavior of the term weighting method may vary depending on different preprocessing techniques in sentiment analysis which considered as a text classification task. In this study, term weighted methods which are newly proposed for various research areas such as information retrieval, text classification and document filtering, performed to investigate effect on results for Twitter sentiment analysis. In feature extraction phase, two different models are used including Bag of Words (BoW) and character level N-gram. The experiments conducted on data sets consist of Turkish and English Twitter feeds. Sentiment classification of Twitter feeds performed using topic model generated with Latent Dirichlet Allocation (LDA) method. The Support Vector Machine (SVM) algorithm is employed in the classification stage. According to the experimental results, the most effective term weighting method that can be used in Twitter sentiment analysis studies is suggested.

Keywords

• Twitter,Sentiment analysis,Term weighting

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