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Commercial establishments like restaurants, service centres and retailershave several sources of customer feedback about products and services, most ofwhich need not be as structured as rated reviews provided by services likeYelp, or Amazon, in terms of sentiment conveyed. For instance, Amazon providesa 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, butunstructured and unlabelled. It might be in the best interests of a businessestablishment to assess the general sentiment towards their brand on theseplatforms as well. This text could be pipelined into a system with a built-inprediction model, with the objective of generating real-time graphs on opinionand sentiment trends. Although such tasks like the one described about havebeen explored with respect to document classification problems in the past, theimplementation described in this paper, by virtue of learning a continuousfunction rather than a discrete one, offers a lot more depth of insight ascompared to document classification approaches. This study aims to explore thevalidity of such a continuous function predicting model to quantify sentimentabout an entity, without the additional overhead of manual labelling, andcomputational preprocessing & feature extraction. This research project alsoaims to design and implement a re-usable document regression pipeline as aframework, Rapid-Rate, that can be used to predict document scores inreal-time.

Rapid-Rate: A Framework for Semi-supervised Real-time Sentiment Trend Detection in Unstructured Big Data