In this thesis, we develop a new probabilistic model for aspect-level sentiment analysis based on POSLDA, a topic classifier that incorporates syntax modelling for better performance. POSLDA separates semantic words from purely functional words and restricts its topic modelling on the semantic words. We take this a step further by modelling the probability of a semantic word expressing sentiment based on its part-of-speech class and then modelling its sentiment if it is a sentiment word. We restructure the popular approach of topic-sentiment distributions within documents and add a few novel heuristic improvements. Our experiments demonstrate that our model produces results competitive to the state of the art systems. In addition to the model, we develop a multi-threaded version of the popular Gibbs sampling algorithm that can perform inference over 1000 times faster than the traditional implementation while preserving the quality of the results.