May 2011
Beginner to intermediate
384 pages
12h 28m
English
Content preview from The Handbook of News Analytics in FinanceBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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5.B INDUSTRY-LEVEL SENTIMENT DATA
I use the following RavenPack data to construct the industry-level indexes.
5.B.1 Company Relevance Score
A score between 0 and 100 that indicates how strongly related the company is to the underlying news story, with higher values indicating greater relevance. For any story that mentions a company, RavenPack provides a relevance score. A score of 0 means the company was passively mentioned once in a story. A score of 100 means the company was predominant in the story and played a well-defined role in the article. The greater the score between 0 and 100, the higher the relevance of the story to the company.
5.B.2 WLE: Word and phrase detection
A score that represents the news sentiment of the given news item according to the WLE classifier, which specializes in identifying positive and negative words and phrases in articles about global equities. Scores can take values of 0, 50, or 100 indicating negative, neutral, or positive sentiment, respectively. This sentiment score is based on RavenPack's Traditional Methodology.
5.B.3 PCM: Projections, corporate news
A score that represents the news sentiment of the given story according to the PCM classifier, which specializes in identifying the sentiment of stories that are only about earnings, developments, and projections news. Scores can take values of 0, 50, or 100 indicating negative, neutral, or positive sentiment, respectively. This sentiment score is based on RavenPack's Expert Consensus Methodology. ...
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