ANALYZING TWITTER DATA OF FIRMS WITH SOCIAL MEDIA MINING

Year 2019, Issue: 23, 237 - 256, 09.04.2019

Büşra Ayan , Mustafa Can , Umman Tuğba Gürsoy

https://doi.org/10.18092/ulikidince.475092

Cited By: 2

Abstract

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

SOSYAL MEDYA MADENCİLİĞİ İLE FİRMALARIN TWITTER VERİLERİNİN İNCELENMESİ

Abstract

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.

Keywords

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

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