  Commercial establishments like restaurants, service centres and retailers
have several sources of customer feedback about products and services, most of
which need not be as structured as rated reviews provided by services like
Yelp, or Amazon, in terms of sentiment conveyed. For instance, Amazon provides
a fine-grained score on a numeric scale for product reviews. Some sources,
however, like social media (Twitter, Facebook), mailing lists (Google Groups)
and forums (Quora) contain text data that is much more voluminous, but
unstructured and unlabelled. It might be in the best interests of a business
establishment to assess the general sentiment towards their brand on these
platforms as well. This text could be pipelined into a system with a built-in
prediction model, with the objective of generating real-time graphs on opinion
and sentiment trends. Although such tasks like the one described about have
been explored with respect to document classification problems in the past, the
implementation described in this paper, by virtue of learning a continuous
function rather than a discrete one, offers a lot more depth of insight as
compared to document classification approaches. This study aims to explore the
validity of such a continuous function predicting model to quantify sentiment
about an entity, without the additional overhead of manual labelling, and
computational preprocessing & feature extraction. This research project also
aims to design and implement a re-usable document regression pipeline as a
framework, Rapid-Rate, that can be used to predict document scores in
real-time.
