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#Conslusion  In this paper I reviewed the work of Zhiyuan Chen and Bing Liu of the University of Chicago, entitled _Mining Topics in Documents: Standing on the Shoulders of Big Data_, presented at the 2014 Conference of Knowledge Discovery and Data Mining. After a brief summary of the paper and its findings, I presented the author's background and related previous work. Liu is a prominent topic modeling and natural language processing researcher and, together with Chen, they have pioneered adapting transfer-learning models for topic mining. I presented the a brief history of transfer learning and its connections to the paper in review. After that I highlighted the some of weaknesses of the paper, mainly the somewhat hard to grasp mathematics, the missing asymptotic analysis and the caveats of using qualitative performance metrics. I closed with outlining the vast usability of this research and potential pathways of improvement - perhaps leading to creating a Bayesian topic -  language model.