The Royal Birth of 2013: Analysing and Visualising Public Sentiment in the UK Using Twitter

TL;DR

This paper develops a framework for analyzing and visualizing public sentiment from Twitter data, comparing dictionary-based and machine learning methods through a UK case study of the 2013 royal birth, highlighting their correlation and the need for faster techniques.

Contribution

It introduces a novel framework that integrates sentiment analysis and visualization, extending machine learning applications for Twitter data aggregation.

Findings

Good correlation between dictionary-based and machine learning methods for large datasets

Framework effectively visualizes public sentiment geographically

Faster big data techniques are needed for rapid analysis

Abstract

Analysis of information retrieved from microblogging services such as Twitter can provide valuable insight into public sentiment in a geographic region. This insight can be enriched by visualising information in its geographic context. Two underlying approaches for sentiment analysis are dictionary-based and machine learning. The former is popular for public sentiment analysis, and the latter has found limited use for aggregating public sentiment from Twitter data. The research presented in this paper aims to extend the machine learning approach for aggregating public sentiment. To this end, a framework for analysing and visualising public sentiment from a Twitter corpus is developed. A dictionary-based approach and a machine learning approach are implemented within the framework and compared using one UK case study, namely the royal birth of 2013. The case study validates the feasibility of the framework for analysis and rapid visualisation. One observation is that there is good correlation between the results produced by the popular dictionary-based approach and the machine learning approach when large volumes of tweets are analysed. However, for rapid analysis to be possible faster methods need to be developed using big data techniques and parallel methods.