Extraction, sentiment analysis and visualization of massive public messages

Abstract

This paper describes the design and implementation of tools to extract, analyze and explore an arbitrarily great amount of public messages from diverse sources. The aim of our work is to flexibly support sentiment analysis by quickly adapting to different use cases, languages, and message sources. First, a highly parallel scraper has been implemented, allowing the user to customize the behavior with scripting technologies and thus being able to manage dynamically loaded content. Then, a novel framework is developed to support agile programming, building and validating a classifier for sentiment analysis. Finally, a web application allows the real-time selection and projection of the analysis results in different dimensions in an OLAP fashion.