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Analyzing the Drivers of Customer Satisfaction Via Social Media
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Yücel, Kadir Kutlu; Koç, Utku
Social media became a great influence force during the last decade. Active social media user population increased with the new generations. Thus, data started to accumulate in tremendous amounts. Data accumulated through social media offers an opportunity to reach valuable insights and support business decisions. The aim of this project is to understand the drivers of customer satisfaction by public sentiments on Twitter towards a financial institution. Data was extracted from the most popular microblogging platform Twitter and sentiment analysis was performed. The unstructured data was classified by their sentiments with a lexicon-based model and a machine learning based model. The outcome of this study showed machine learning based model successfully overcame the language specific problems and was able to make better predictions where lexicon-based model struggled. Further analysis was performed on the extreme daily average sentiment scores to match these days with prominent events. The results showed that the public sentiment on Twitter is driven by three main themes; complaints related to services, advertisement campaigns, and influencers’ impact.
Understandng Emotion Fluctuations Using Social Media
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Ceran, Serkan; Akpınar, Ezgit
During the last years, the importance of social media is increasing in an amazing way. In this paper, we looked at one such popular microblog platform called Twitter and build models for classifying “tweets” into some specific emotion. We used Turkey’s twitter data in order to explore the change in emotions over time using sentiment analysis. Using LIWC dictionary database, we conducted an emotion analysis of approximately 2.2 million tweets. We tracked how emotions evolve over time based on the prominent events in and or related to Turkey. Our results showed that there is a significant relationship between emotions and prominent events. We also analyzed the correlation between these emotions and the dollar exchange and made a predictive modeling experiment.
Underlying the Bias for Human Music Evaluation
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Yıldırım, Burak; Çakar, Tuna
Predictive analysis is the process of using data analytics to predict the future over historical data. Data analytics is the use of statistical modelling and / or machine learning methods to measure the future. In short, it is one of the data mining techniques for predictive analysis that focuses on creating a predictive model for the future by extracting relationships from the data.

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