BRAND CRISIS IN SOCIAL MEDIA: CASE STUDY USING SENTIMENT ANALYSIS
Abstract
Purpose- It is necessary to develop different crisis management strategies in order to understand their own images on consumers and to change the negative attitudes and perceptions to the positive ones. In this study, social media users' attitudes towards two different brands during the crisis were examined. The main purpose of this study is to give suggestions regarding the communication strategies that brands will develop through social media.
Methodology- Based on the text mining method, sentiment analysis was performed with Google Natural Language Processing on the data obtained from the Twitter which is a social media platform.
Findings- According to the results of the sentiment analysis conducted for two different brands, it is seen that social media users express a positive attitude to one of the firms, while they express a negative attitude to the other one.
Conclusion- In this study, the reasons of different attitudes of social media users were discussed. The reasons for this difference are thought to be because of the different sectors the companies belong to, different product category and their pricing strategy.
Keywords
References
- Akdağ, M. (2005). Halkla ilişkiler ve kriz yönetimi. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (14), 1-20.
- Alpaydın, E. (2000). Zeki veri madenciliği: ham veriden altin bilgiye ulaşma yöntemleri. Bilişim 2000 Eğitim Semineri, Boğaziçi Üniversitesi, İstanbul.
- Arıcı, K. (2001). Krizler firsata dönüştürülebilir. Türk-Koop Dergisi, 17, 345.
- Burgess, M. (2016). Samsung blames drop in profits on Galaxy Note 7 problems. Erişim: https://www.wired.co.uk/article/samsung-profits-drop-note-7.
- Can, H. (1994). Organizasyon ve yönetim. Siyasal Kitapevi, 3. Baskı, Ankara.
- Çiçekli, İ. (1999). Tercüme kaliplarinin makina öğrenmesi teknikleri ile tercüme örneklerinden öğrenilmesi. TÜBİTAK Projesi, Proje No: EEEAG-244(197E011).
- Delen, D., Crossland, M.D. (2008). Seeding the survey and analysis of research literature with text mining. Expert Systems with Applications. 34, 1707–1720.
- Delibaş, A. (2008). Doğal dil işleme ile Türkçe yazim hatalarinin denetlenmesi. Yüksek Lisans Tezi, İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Ana Bilim Dalı, İstanbul, Türkiye, 1-5.
Details
Primary Language
English
Subjects
-
Journal Section
Research Article
Authors
Meltem Kiygi-calli
This is me
0000-0002-2979-9309
Publication Date
September 30, 2018
Submission Date
July 25, 2018
Acceptance Date
September 27, 2018
Published in Issue
Year 2018 Volume: 5 Number: 3