Danışmanlı ve yarı danışmanlı öğrenme kullanarak doküman vektörleri tabanlı tweetlerin duygu analizi

Abstract

İnternetin günlük hayatımızdaki artan kullanımı ile beraber sosyal medya organlarının gelişimi de paralellik göstermektedir. Mikroblog adı verilen facebook ve twitter benzeri uygulamaları ile anlık duyguları ve düşünceleri ifade etmek son derece yaygın bir hale gelmiştir. Mikroblog sitelerinin en yaygın kullanıma sahip olanlarından birisi de Twitter uygulamasıdır. Twitter üzerinden paylaşılan mesajlar bir ürün ya da hizmet hakkında olabileceği gibi bir kişiyle ilgili bir yorumda olabilmektedir. Yapılan yorumun belirtmek istediği anlamı ve duyguyu belirleyebilmek son dönemdeki gözde konulardan biridir. Bir ürün ya da hizmet hakkında yapılan binlerce yorumun tek tek okunup anlamlandırılması ve yorumlayanların fikirlerinin sınıflandırılması geleneksel yöntemlerde oldukça zaman ve emek alan bir alandır. Gerek makine öğrenmesi ve derin öğrenme algoritmalarındaki gelişmeler gerekse de bunları işleyip yorumlayacak bilgisayar sistemlerinin gelişimine parallel olarak milyonlarca veri üzerinde duygu sınıflandırılması mümkün hale gelmiştir. Gerçekleştirdiğimiz çalışmada Türkçe ve İngilizce tivitler üzerinde duygusal sınıflandırma çalışması gerçekleştirilmiştir. Döküman vektörleri (Doc2Vec) kullanılarak yapılan çalışmada hem DBoW ve DM gibi iki farklı döküman vektörü yönteminin çalışması hemde Yarı Danışmanlı ve Danışmanlı öğrenmenin etkileri araştırılmıştır. Çalışma sonuçları doğruluk, kesinlik, anma, özgünlük ve F-ölçütü metrikleri ile raporlanmıştır. Gerçekleştirilen çalışma sonucunda Yarı Danışmanlı öğrenme yöntemi hem Türkçe hemde İngilizce veri kümesinde Danışmanlı öğrenmeye göre daha başarılı sonuçlar elde etmiştir.

Keywords

• Yarı danışmanlı öğrenme,danışmanlı öğrenme,Doc2Vec,duygu analizi,makine öğrenmesi,doğal dil işleme,derin öğrenme

Sentiment analysis of tweets based on document vectors using supervised learning and semi-supervised learning

Abstract

The increasing presence of the Internet in the daily life leads to proliferation of social media. Microblogging applications such as Facebook and Twitter let their users to express their feelings and emotions in a short form of digital content including text, picture, and video. Twitter is one of the most popular microblogging applications. Tweets posted on twitter may contain positive or negative comments on a product, service or an individual. The analysis of the sentiments behind the tweets has been a popular research topic recently. Considering the time required to read and analyze a vast amount of tweets regarding a given product or service, manual classification of tweets is a complex and time consuming process. Recent machine learning and deep learning algorithms and the developments in the computer systems enable classification of the huge amount of data according to their sentiments in parallel. In this study, we have carried out sentiment analysis on tweets with both Turkish and English content. We have employed Document Vectors (Doc2Vec) along with DBoW and DM and analyzed the performance of semi-supervised and supervised learning methods. We have reported the performance in terms of accuracy, precision, recall, specificity and F-score metrics. We have concluded that semi-supervised learning outperforms supervised learning for both Turkish and English datasets.

Keywords

• Semi-supervised learning,supervised learning,Doc2Vec,sentiment analysis,machine learning,natural language processing,deep learning

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