High-Volume Business News : Detecting Topic-Specific Sentiments to Account for Brand Ratings and Stock Returns

Due to the highly voluminous, heterogeneous, and unstructured nature of global business news streaming at a fast pace, it has become increasingly difficult for marketing executives, corporate communications managers, and market analysts to make sense and track news media stories addressing the company and its brand. This paper responds to this challenge by developing an integrated methodology for identifying and tracking company-specific news topics as well as topic-specific sentiments from news media text data. The main contribution and novelty of the methodology lies in its ability to identify not only company-specific news topics but also to track the topic-specific sentiments (i.e. positive vs. negative tone/valence) over time. This extends prior methods and models, which have only focused on the identification of either topics or aggregate sentiments from news text data, not being able to identify and track the disaggregated topic-specific sentiments (i.e., whether the news media stories treat the firms in a positive vs. negative tone for each separate topic, over time). In addition to developing the integrated topic-sentiment model, we demonstrate the utilization value of the methodology for executives, in terms of its predictive ability for firm performance metrics. That is, we demonstrate, with a set of time-series regression models, that the topic-specific sentiments identified by the model (on Thomson Reuters Newswire data with over 300,000 news items) have the ability to predict firm performance in terms of brand ratings as well as stock returns