Sosyal Medya Analitiğinde Makine Öğrenmesi Uygulamaları: Literatür İncelemesi

Year 2021, Volume: 16 Issue: 61, 95 - 127, 31.01.2021

Birce Dobrucalı , Burcu İlter

https://doi.org/10.19168/jyasar.687093

Abstract

Sosyal medya platformlarından kullanıcı tarafından oluşturulan verilerin toplanması ve analiz edilmesini ifade eden sosyal medya analitiği (SMA), tüketici içgörüleri elde etmeye çalışan araştırmacıların ve uygulayıcıların ilgi odağındadır. Bu alan, makine öğrenimi algoritmalarının yüksek hacimli ve karmaşık verileri uygun maliyetli bir şekilde işleyerek kayda değer içgörüleri yakalama kapasitesine paralel olarak çok yönlü bir şekilde büyümesini sürdürmektedir. Makine öğrenimi uygulamaları, sosyal medya analitiğinin geleceğini yeniden şekillendirebilecek verimli bir alan olarak dikkat çektiğinden, mevcut trendleri ve yaklaşımları bütünleştirici bir çerçevede anlamaya ihtiyaç vardır. Bu bağlamda, mevcut çalışma sosyal medya analitiği alanındaki makine öğrenimi uygulama trendlerini ve yaklaşımlarını bütünleştirici bir çerçevede sunmayı amaçlamaktadır. 2013-2019 yılları arasında hakemli bilimsel dergilerde yer alan ve işletme, yönetim ve bilgisayar bilimleri alanında yayınlanan 42 bilimsel makale, görsel metin madenciliğine dayalı sistematik literatür taraması yöntemi ile analiz edilmiştir. Sonuçlar beş farklı araştırma kümesini ortaya çıkarmıştır: (1) inceleme siteleri, (2) mikrobloglar, (3) sosyal ağ siteleri, (4) içerik toplulukları, (5) platformlar arası çalışmalar. Mevcut çalışma, alanın entelektüel yapısı hakkındaki anlayışı geliştirmek, alanın önde gelen çalışmalarına dikkat çekmek, gelecekteki araştırmaların daha iyi konumlandırılmasına yönelik olarak alandaki boşlukları ve yeni araştırma alanlarını belirlemek açısından önemli bir rol oynamaktadır.

Keywords

sosyal medya analitiği, makine öğrenmesi, kullanıcı katkılı içerik

Machine Learning Applications in Social Media Analytics: A State-of-Art Analysis

Abstract

Social media analytics (SMA), referring to the collection and analysis of user generated data from social media platforms, attract attention of both researchers and practitioners striving to derive consumer insights. The SMA domain grows multifariously, with a highlight on the capability of machine learning algorithms in capturing noteworthy insights through processing high-volume and complex data in a cost effective way. As machine learning applications draw attention as a fertile area that may re-shape the future of SMA, there is a need to comprehend trends and approaches in an integrative framework. Accordingly, this study aims to present an integrative framework by portraying machine learning application trends and approaches in SMA. 42 scientific articles published in refereed scientific business, management, and computational science journals between the years 2013 and 2019 are analyzed via systematic literature review based on visual text mining method (SLR-VTM). The results revealed five distinctive research clusters as: (1) review sites, (2) microblogs, (3) social networking sites, (4) content communities, (5) cross-media. This analysis plays a crucial role for enhancing our understanding regarding the intellectual structure of the field, acknowledging the leading studies of the domain, better positioning future research, and determining gaps and new paths for researchers.

Keywords

social media analytics, machine learning, user-generated content

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