An analysis of customer complaints in the telecommunications sector via BTK and digital platforms

In this study, customer complaints shared on digital platforms were systematically analyzed. Data were collected from sources such as Şikayetvar, Google Play, and the App Store and processed using natural language processing (NLP) techniques. The primary objective was to identify the main problem areas in the telecommunications sector by applying sentiment analysis, topic modeling, and keyword extraction methods to the complaints. The data preprocessing phase involved cleaning the text data, handling missing values, and removing irrelevant content to ensure reliable analysis. Sentiment analysis was used to assess customer satisfaction levels, while topic modeling techniques helped uncover the most frequently mentioned issues. In addition to digital platforms, official complaint data obtained from the Information and Communication Technologies Authority (BTK) were also examined. A comparative analysis was conducted between digital complaints and official complaints submitted to BTK, focusing on factors such as complaint themes, resolution times, and customer satisfaction rates. The comparison revealed that complaints shared on digital platforms tend to spread more rapidly and are often less structured, whereas BTK complaints are more formal, detailed, and processed through standardized procedures. These findings highlight the complementary roles of digital platforms and official regulatory channels in capturing customer feedback. The study demonstrates that integrating insights from both sources can contribute to more effective customer service strategies and informed decision-making in the telecommunications sector. Keywords: Telecommunication, Complaint Analysis, Natural Language Processing, Customer Satisfaction, Digital Transformation