Büşra AYAN, Mustafa CAN, Umman tuğba GÜRSOY

SOSYAL MEDYA MADENCİLİĞİ İLE FİRMALARIN TWITTER VERİLERİNİN İNCELENMESİ

Bu çalışma, farklı sektörlerde faaliyet gösteren rakip firmaların Twitter verilerini analiz ederek, firmaların Twitter verilerinin firmalara göre anlamlı bir uyum gösterip göstermediğinin tespit edilmesini, firmaların Twitter’da paylaştıkları içeriklerin kümelenmesini ve hangi içerik kümesinin en fazla etkileşime yol açtığının belirlenmesini amaçlamaktadır. Bu kapsamda, 2017 yılı boyunca kozmetik, elektronik ve pazaryeri sektörlerinde faaliyet gösteren rakip firmalar tarafından paylaşılan Twitter verileri, Sosyal Medya Madenciliği süreci izlenerek analiz edilmiştir. Firmaların Twitter verilerinin firmalara göre anlamlı bir uyum gösterip göstermediği Uygunluk Analizi ile tespit edilmiştir. Firmaların Twitter paylaşımları ise Metin Madenciliği ön işleme metotlarından faydalanılarak “Özel Teklif”, “Yarışma & Etkinlik”, “Ürün”, “Sosyal”, “Destek & Geri Bildirim” ve “Özel Etkileşim” kategori başlıklarıyla kümelenmiştir. Firmaların elde ettikleri etkileşimlerin büyük bir çoğunluğunun azınlıktaki paylaşımlardan gelmesi sebebi ile hangi içerik kümesinin en fazla etkileşime yol açtığı Pareto İlkesi yardımı ile belirlenmiştir.

Anahtar Kelimeler:

sosyal medya, sosyal medya madenciliği, metin madenciliği, pareto ilkesi, uygunluk analizi

ANALYZING TWITTER DATA OF FIRMS WITH SOCIAL MEDIA MINING

This study aims to determine whether Twitter data of the firms has a significant correspondence with respect to the firms, to cluster Twitter feeds of the firms and to find out which cluster has the maximum interaction through analyzing the Twitter data of the rival firms operating in different sectors. In this context, Twitter data shared by competitors operating in the cosmetics, electronics and marketplace sectors during 2017 were analyzed by following the process of Social Media Mining. The significant correspondence of Twitter variables of the firms was determined by the Correspondence Analysis. Twitter feeds of the firms were clustered with categories “Special Offer”, “Competition & Event”, “Product”, “Social”, “Support & Feedback” and “Special Interaction” by using a number of Text Mining pre-processing methods. Since the majority of the interactions obtained by the firms came from the minority of the feeds, which cluster received more interaction was analyzed with the help of the Pareto Principle.

Keywords:

social media, social media mining, text mining, pareto principle, correspondence analysis,

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