EXPERT OPINION AND COHERENCE BASED TOPIC MODELING

In this paper, we propose a novel algorithm that rearrange the topic assignment results obtained from topic modeling algorithms, including NMF and LDA. The effectiveness of the algorithm is measured by how much the results conform to expert opinion, which is a data structure called TDAG that we defined to represent the probability that a pair of highly correlated words appear together. In order to make sure that the internal structure does not get changed too much from the rearrangement, coherence, which is a well known metric for measuring the effectiveness of topic modeling, is used to control the balance of the internal structure. We developed two ways to systematically obtain the expert opinion from data, depending on whether the data has relevant expert writing or not. The final algorithm which takes into account both coherence and expert opinion is presented. Finally we compare amount of adjustments needed to be done for each topic modeling method, NMF and LDA