Data cleansing, restructuring
that data – whether stock market tick data, corporate actions,
economic statistics or unstructured news XML – is consistently
time stamped, formatted, cleansed and structured to meet the needs
of clients demanding the highest level of data reliability and consistency.
Collection of massive amounts of news
RavenPack’s data warehouses connect to
numerous high-frequency news feeds while leveraging massive computing power and
storage capabilities. Decades of historical news on multi-terabyte sized databases
are standard implementations in the company’s infrastructure.
Parallel databasing and analysis
Just as RavenPack’s servers are able to
provide managed storage for many high frequency data feeds,
they are capable of parallel, real-time analysis of such feeds.
Analysis consists of sentiment scoring, data normalization, metadata
application (subject, genre, facts, and sentiment tags), content
structuring, relationship identification and others. These processes
run in millisecond time on all events for all feeds in parallel.
Data classification and tagging
RavenPack has accumulated significant expertise
in classifying and tagging data streams, particularly those related
to financial news. Dozens of automated classifiers identify whether
stories are positive, negative or neutral using varied linguistic
techniques developed by RavenPack’s in-house experts. Additionally,
classifiers identify, extract and structure story substance ranging
from aboutness, genre, key actors, locations, impact, relevance,
facts and figures, and others.
High availability data retrieval systems
Subscribed users leverage the company’s
data-mining capabilities that allow for instant retrieval of time-series
data and analysis via our API. RavenPack’s proprietary, hosted
data structures enable access to massive, high-maintenance databases
through simple, zero-cost web interfaces.
Customized news sentiment and volume
indicators based on client's area of interest
Leveraging the company’s data infrastructure
against extensive news archives from diverse sources, RavenPack
is able to construct news sentiment and volume indicators for varied
markets, asset classes, financial themes or individual companies.
Extensive linguistic experience empowers classification methodologies
that are both robust and modular.
For more information on RavenPack, email email@example.com.