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    Brand Unhappiness on Social Media
    (Springer Science and Business Media B.V., 2018) Uyar, Kumru; Ozyirmidokuz, Esra Kahya; Stoica, Eduard Alexandru
    Social media is one of the places where brands can communicate with their customers and potential customers easily and fast. Although there are lots of advantages to using social media including low cost, being able to measure customers’ feelings, having fast and easy access to consumers, etc., it is very important for a firm not to make any mistakes on social media, because the online brand is lonely against to the crowd. Customers can easily give feedback about brands. Unhappy customers can share their opinion with the masses through social media and negatively affect the firm. It is important for firms to control and manage brand unhappiness. Firms should understand brand unhappiness factors in order to increase the happiness of their customers. This research aims to discover a better understanding of the brand unhappiness concept by examining the online social media feedback of customers. For this purpose, 17 different firms’ Facebook customer comments are analyzed. Therefore, the brand unhappiness factors on social media are extracted. © 2018, Springer International Publishing AG.
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    Methodological Approach for Messages Classification on Twitter Within E-Government Area
    (Springer Science and Business Media B.V., 2018) Stoica, Eduard Alexandru; Ozyirmidokuz, Esra Kahya; Uyar, Kumru; Pitic, Antoniu Gabriel
    The constant growth in the numbers of Social Media users is a reality of the past few years. Companies, governments and researchers focus on extracting useful data from Social Media. One of the most important things we can extract from the messages transmitted from one user to another is the sentiment—positive, negative or neutral—regarding the subject of the conversation. There are many studies on how to classify these messages, but all of them need a huge amount of data already classified for training, data not available for Romanian language texts. We present a case study in which we use a Naïve Bayes classifier trained on an English short text corpus on several thousand Romanian texts. We use Google Translate to adapt the Romanian texts and we validate the results by manually classifying some of them. © 2018, Springer Nature Switzerland AG.