RavenPack has developed linguistic analytics that process the textual input of news stories to determine quantitative sentiment scores. These scores allow us to incorporate information about the volume and nature of news into quantitative models. We give a brief description of how these have been created.
As a news story is received from a newswire it is tagged to record various linguistic aspects. One particular aspect is a story's “aboutness”. This incorporates the entities to which the story applies, the subjects it covers, and the market to which it is relevant.
This analysis is applied to tens of thousands of stories per day aggregated from RavenPack's compilation of diverse and respected sources of news.
RavenPack's sentiment classifiers detect story type as a preliminary step to distinguishing the story as being “positive” (POS), “negative” (NEG), or “neutral” (NEU) relative to a specific market or asset class. There are two main methods for detecting sentiment. The Expert Consensus Method uses financial experts' tagging of several thousand stories as POS, NEG, or NEU to train a Bayes Classifier which discerns rules from the training set to imitate the experts' tagging. The Traditional Method maps specific words or phrases to pre-defined sentiment values.
The tagging of individual stories can be used to aggregate sentiment scores of specific companies, such as the ...