Polypus: a Big Data Self-Deployable Architecture for Microblogging Text Extraction and Real-Time Sentiment Analysis

Abstract

In this paper we propose a new parallel architecture based on Big Data technologies for real-time sentiment analysis on microblogging posts. Polypus is a modular framework that provides the following functionalities: ( 1) massive text extraction from Twitter, (2) distributed non-relational storage optimized for time range queries, (3) memory-based intermodule buffering, (4) real-time sentiment classification, (5) near real-time keyword sentiment aggregation in time series, (6) a HTTP API to interact with the Polypus cluster and ( 7) a web interface to analyze results visually. The whole architecture is self-deployable and based on Docker containers.

Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications

“…Papers related to microblogging and sentiment analysis have also been included in this category. Applications related to microblogging include data management [147] and text extraction and real-time sentiment analysis [216].…”