Current topic models often suffer from discovering topics not matching human intuition, unnatural switching of topics within documents and high computational demands. We address these shortcomings by proposing a topic model and an inference algorithm based on automatically identifying characteristic keywords for topics. Keywords influence the topic assignments of nearby words. Our algorithm learns (key)word-topic scores and self-regulates the number of topics. The inference is simple and easily parallelizable. A qualitative analysis yields comparable results to those of state-of-the-art models, but with different strengths and weaknesses. Quantitative analysis using eight datasets shows gains regarding classification accuracy, PMI score, computational performance, and consistency of topic assignments within documents, while most often using fewer topics.