Distributed Real-Time Sentiment Analysis for Big Data Social Streams

TL;DR

This paper introduces Sentinel, a distributed real-time sentiment analysis system built on Apache Storm, capable of processing high-speed social media streams efficiently while maintaining high accuracy.

Contribution

Sentinel is a novel distributed system that combines parallel decision tree learning with stream summarization for real-time social media analytics.

Findings

Sentinel effectively processes Twitter streams with low latency.

The Vertical Hoeffding Tree achieves high accuracy in distributed environments.

Sentinel demonstrates scalable performance on big social data streams.

Abstract

Big data trend has enforced the data-centric systems to have continuous fast data streams. In recent years, real-time analytics on stream data has formed into a new research field, which aims to answer queries about what-is-happening-now with a negligible delay. The real challenge with real-time stream data processing is that it is impossible to store instances of data, and therefore online analytical algorithms are utilized. To perform real-time analytics, pre-processing of data should be performed in a way that only a short summary of stream is stored in main memory. In addition, due to high speed of arrival, average processing time for each instance of data should be in such a way that incoming instances are not lost without being captured. Lastly, the learner needs to provide high analytical accuracy measures. Sentinel is a distributed system written in Java that aims to solve this challenge by enforcing both the processing and learning process to be done in distributed form. Sentinel is built on top of Apache Storm, a distributed computing platform. Sentinels learner, Vertical Hoeffding Tree, is a parallel decision tree-learning algorithm based on the VFDT, with ability of enabling parallel classification in distributed environments. Sentinel also uses SpaceSaving to keep a summary of the data stream and stores its summary in a synopsis data structure. Application of Sentinel on Twitter Public Stream API is shown and the results are discussed.