SOSYAL MEDYA ANALİTİĞİ İLE DEĞER YARATMA: DUYGU ANALİZİ İLE GELECEĞE YÖNELİM

Year 2018, Volume: 5 Issue: 3, 797 - 811, 27.12.2018

Burcu Akın , Umman Tuğba Gürsoy Şimşek

https://doi.org/10.30798/makuiibf.435804

Cited By: 10

Abstract

Müşterisi
sosyal medya kullanıcıları olan işletmelerin bu kanallardan ürünleri hakkında
yazılan iletileri toplayıp analiz etme, bunları satış rakamları ile
karşılaştırma veya ilişkilendirme, gelecek için stratejiler geliştirme gibi
imkanları vardır. Bu çalışmada uygulanan yöntem ile bir televizyon kanalına ait
seyirci görüşlerinin sosyal medya üzerinden derlenerek kanal için faydalı bilgi
elde edilmesi ve bu bilgilerin kanal için bir karar destek unsuru olması
hedeflenmiştir. Bu amaçla bir TV kanalında yayınlanan programlar hakkında Kasım
- Haziran 2017 tarihleri arasında Twitter’dan paylaşılan iletiler derlenmiş, duygu
analizi tekniği ile her ileti pozitif, negatif ya da nötr olarak
etiketlenmiştir. Bu periyoddaki yayın akışındaki tüm programlar için kaç
pozitif, kaç negatif ve kaç nötr tweet atıldığı bilgilerine bağlı olarak
reyting değeri incelenmiştir.

Keywords

Duygu analizi, sosyal medya analitiği, regresyon analizi

SOCIAL MEDIA ANALYTICS: VALUE CREATION WITH SENTIMENT ANALYSIS

Abstract

Businesses with customers who are users of social media, have the
option of collecting and analyzing data from social media, comparing or
correlating them with sales figures, and developing strategies for the future.
By using the method applied in this study, it is aimed to obtain beneficial
information for the channel by compiling the views of spectators belonging to a
television channel through social media. Also, the study will provide a
decision support tool for the channel. For this purpose, Twitter is chosen as
social media channel and tweets which are about programs broadcast on a TV
channel, were compiled from November 2016 to June 2017. Each tweet is labeled
as positive, negative, or neutral by a novel sentiment analysis method. Then, for
all programs in this period, the rating was examined based on positive,
negative and neutral tweets. 

Keywords

Sentiment analysis, social media analytics, regression analysis

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