Programming is a complex cognitive activity that often poses a significant challenge for beginners. Beyond the cognitive dimension, understanding the role of affective states, such as emotions, attitudes, and motivation, in the learning process can provide valuable insights for educators to improve teaching methodologies and curriculum design. Despite this importance, few studies relate the cognitive aspects of beginning programmers to their affective states in the context of problem-solving. In this article, we explore the affective states reported by novice programmers in introductory programming courses, relating them to programming concepts and success rates during problem-solving activities. Furthermore, we apply machine learning algorithms to detect affective states in novice programmers, considering both student and task characteristics. Our initial results allow us to understand the affective states most prevalent in problems associated with each programming concept, as well as to reveal a significant association between affective states and the performance of novice programmers. Positive affective states, including pleasure, motivation, and challenge, were positively correlated with better performance outcomes. On the other hand, negative affective states such as frustration, anxiety, and boredom impaired performance. Additionally, our algorithms demonstrated good performance in detecting affective states.