Monitoring Events of Market Competitors: A Text Mining Method for Analyzing Massive Firm-Generated Social Media

Abstract

Understanding how competitors act in a market is a critical component of strategic decision-making. In this paper, we propose a method to extract firm events from the textual content generated by firms in the market and explore the competitive relationships among firms based on the spatiotemporal homogeneity of events of different firms. To this end, we first introduce experts to define a series of business events based on the content of corporate-generated texts; then, we propose algorithms to extract and enrich the feature words (triggers) of these business events to form better event classifiers. We subsequently use these classifiers to identify the business events recorded in all online texts published by companies. Finally, based on these results, we can obtain a sequence of activities/events for each firm in the market, which can be used to identify the evolutionary patterns of firms’ behavior in the market, as well as their potential competitive relationships. Considering that competition between companies in the market appears to be continuous at the strategic level, but the implementation of competitive behavior is expressed through their “events” in the market, identifying whether companies are “competing” in the market requires timely observation of the information about “events” in the market. However, obtaining accurate market information is complex and costly. Therefore, this study provides a way to bridge the gap between social media data and market competition “events”.