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In the era of big data, huge volumes of data
are generated from online social networks, sensor networks, mobile devices, and
organizations’ enterprise systems. This phenomenon provides organizations with unprecedented
opportunities to tap into big data to mine valuable business intelligence. However,
traditional business analytics methods may not be able to cope with the flood of
big data. The main contribution of this paper is the illustration of the development
of a novel big data stream analytics framework named BDSASA that leverages a probabilistic
language model to analyze the consumer sentiments embedded in hundreds of millions
of online consumer reviews. In particular, an inference model is embedded into the
classical language modeling framework to enhance the prediction of consumer sentiments.
The practical implication of our research work is that organizations can apply our
big data stream analytics framework to analyze consumers’ product preferences, and
hence develop more effective marketing and production strategies.