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Parallel Sentiment Analysis with Storm
Abstract
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 analytics framework named ASMF that leverages a probabilistic language model to analyze the consumer sentiments embedded in hundreds of millions of online product 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 analytics framework to analyze consumers’ product preferences, and hence develop more effective marketing and production strategies.
... Real-time topic sentiment analysis is imperative to meet the strict time and space constraints to efficiently process streaming data [6]. Wang More recent research [23] [24] have proposed big data stream processing architectures. The first work in 2015 [23] proposed a multi-layered storm based approach for the application of sentiment analysis on big data streams in real time and the second work in 2016 [24] proposed a big data analytics framework (ASMF) to analyze consumer sentiments embedded in hundreds of millions of online product reviews. ...
... Wang More recent research [23] [24] have proposed big data stream processing architectures. The first work in 2015 [23] proposed a multi-layered storm based approach for the application of sentiment analysis on big data streams in real time and the second work in 2016 [24] proposed a big data analytics framework (ASMF) to analyze consumer sentiments embedded in hundreds of millions of online product reviews. Both approaches leverage probabilistic language models by either mimicking "document relevance": with probability of the document generating a user provided query term found within the sentiment lexicon [23] or by adapting a classical language modeling framework to enhance the prediction of consumer sentiments [24]. ...
... The first work in 2015 [23] proposed a multi-layered storm based approach for the application of sentiment analysis on big data streams in real time and the second work in 2016 [24] proposed a big data analytics framework (ASMF) to analyze consumer sentiments embedded in hundreds of millions of online product reviews. Both approaches leverage probabilistic language models by either mimicking "document relevance": with probability of the document generating a user provided query term found within the sentiment lexicon [23] or by adapting a classical language modeling framework to enhance the prediction of consumer sentiments [24]. However, the major limitation of their works is both the proposed frameworks have never been implemented and tested under an empirical setting or in real time. ...
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