Mining Significant Terminologies in Online Social Media Using Parallelized LDA for the Promotion of Cultural Products *

Despite the growing popularity of online social media, there are very few research efforts to use online social media to study market strategies for the promotion of cultural products. With online content being largely unregulated, Latent Dirichlet Allocation (LDA) provides a useful mechanism for organizing textual data and deriving conclusions about the subject matter. In this paper, we introduce a parallelized LDA, called pLDA, to analyze clustered textual data in online social media. We use pLDA to infer the posterior of latent topics over documents and words, and identify significant terminologies that describe the vast number of posts. Making use of sentiment analysis, we are able to further make suggestions about the relevant topics for promoting cultural products. Finally, we use a case study of the music industry to demonstrate how the most relevant aspects to artist popularity can be derived.