The Royal Birth of 2013:Analysing and Visualising Public Sentiment in the UK Using Twitter
TABLE I: Summary of results from case study
['[BOLD] Country', '[BOLD] 2.2cmNo. of Tweets', '[BOLD] Dictionary-based Approach  [BOLD] NPSS', '[BOLD] Dictionary-based Approach  [BOLD] PSS', '[BOLD] Dictionary-based Approach', '[BOLD] Dictionary-based Approach', '[BOLD] Machine Learning Approach', '[BOLD] Machine Learning Approach', '[BOLD] Machine Learning Approach', '[BOLD] Machine Learning Approach']
The Parser module trimmed the corpus, and the fine level of geographic details, namely latitude and longitude, was used to map the tweets onto the county and the country of origin using the Global Administrative Areas (GADM) (.shp) The dictionary-based and machine learning approaches were used for sentiment analysis and the aggregation of public sentiment was performed. The highest volume of tweets was obtained from England, followed by Wales and then Scotland. The smallest number of tweets during the three day period was from N. Ireland. On July 22 and July 23 the dictionary-based approach estimates England to have had the highest PSS compared to the other countries. Surprisingly, on the day after the birth, England dropped to the third place. On the other hand, the machine learning approach places England consistently in third place. The machine learning approach estimates Wales to have the highest PSS on all three days.