Generative artificial intelligence-powered sentiment analysis in the context of brand language and content management

Towards the end of the twentieth century, the rapid development of information and communication technologies has brought about radical transformations in the social, economic and cultural structure of societies. This thesis uses artificial intelligence-supported sentiment analysis techniques to analyze brands' communication strategies on social media. The study reveals how language and cultural differences are reflected in communication strategies by analyzing the sentiment of a particular brand's posts on English and Turkish social media accounts under five different categories such as promotion, inspirational, informative, question and celebration. The results of the analysis examine how the brand adopts a communication strategy according to different language groups. This study provides in-depth insights in the field of marketing and public relations by using the capacity of artificial intelligence technologies to perform fast and effective analysis on large data sets. The data obtained from the brand's social media posts contributes significantly to strategic decision-making processes and enables more effective management of marketing campaigns. As a result, this thesis provides insights to help brands develop more effective communication strategies by taking into account language and cultural characteristics and aims to provide guidance for future marketing strategies.