2015 DynamicTopicModelingforMonitori

• (Zhang et al., 2015) ⇒ Hao Zhang, Gunhee Kim, and Eric P. Xing. (2015). “Dynamic Topic Modeling for Monitoring Market Competition from Online Text and Image Data.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783293

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Dynamic+Topic+Modeling+for+Monitoring+Market+Competition+from+Online+Text+and+Image+Data
• http://dl.acm.org/citation.cfm?id=2783258.2783293&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; dynamic topic models; economics; market competition; probabilistic algorithms; text and images

Abstract

We propose a dynamic topic model for monitoring temporal evolution of market competition by jointly leveraging tweets and their associated images. For a market of interest (e.g. luxury goods), we aim at automatically detecting the latent topics (e.g. bags, clothes, luxurious) that are competitively shared by multiple brands (e.g. Burberry, Prada, and Chanel), and tracking temporal evolution of the brands' stakes over the shared topics. One of key applications of our work is social media monitoring that can provide companies with temporal summaries of highly overlapped or discriminative topics with their major competitors. We design our model to correctly address three major challenges: multiview representation of text and images, modeling of competitiveness of multiple brands over shared topics, and tracking their temporal evolution. As far as we know, no previous model can satisfy all the three challenges. For evaluation, we analyze about 10 millions of tweets and 8 millions of associated images of the 23 brands in the two categories of luxury and beer. Through experiments, we show that the proposed approach is more successful than other candidate methods for the topic modeling of competition. We also quantitatively demonstrate the generalization power of the proposed method for three prediction tasks.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 DynamicTopicModelingforMonitori	Eric P. Xing Gunhee Kim Hao Zhang			Dynamic Topic Modeling for Monitoring Market Competition from Online Text and Image Data				10.1145/2783258.2783293		2015