STracker: A framework for identifying sentiment changes in customer feedbacks

Companies and organizations monitor customer satisfaction by collecting feedback through Likert scale questions and free-text responses. Freely expressed opinions, not bound to fixed questions, provide a detailed source of information that organizations can use to improve their daily operations. The organization's quality assurance review processes require a timely follow-up on these customer opinions. However, solutions often address the analytics of textual information with topic discovery and sentiment analysis for a fixed time period. These frameworks also tend to focus on serving the purpose of a specific domain and terminology. In this study, we focus on a facilitation service to track discovered topics and their sentiments over time. This service is generic and can be applied to different domains. To evaluate the capabilities of the framework, we used two datasets with opposite types of wording. The study shows that the framework is capable of discovering similar topics over time and identifying their sentiment changes.