Guang Xiang, Zeyu Zheng, Miaomiao Wen, Jason Hong, Carolyn Rose, and Chao Liu

International AAAI Conference on Weblogs and Social Media (ICWSM)

Work in Progress

Merger and Acquisition (M&A) prediction has been an interesting and challenging research topic in the past a few decades. However, past work has only adopted numerical features in building models, and yet the valu-able textual information from the great variety of so-cial media sites has not been touched at all. To fully explore this information, we used the profiles and news articles for companies and people on TechCrunch, the leading and largest public database for the tech world, which anybody can edit. Specifically, we explored topic features via topic modeling techniques, as well as a set of other novel features of our design within a machine learning framework. We conducted experiments of the largest scale in the literature, and achieved a high true positive rate (TP) between 60%to 79.8% with a false positive rate (FP) mostly between 0% and 8.3% over company categories with a small number of missing at-tributes in the CrunchBase profiles.