Over the past 6 months I've been creating a system to monitor sentiment on Twitter, using nothing but freely available technologies. The culmination of this study has resulted in the discovery and subsequent proof of regular fluctuations in sentiment within the UK. I highly recommend that you read the full study included in this entry; it's an interesting area of research and one which I believe warrants further analysis.
This study sets out to experiment with the possibility of using free Web technologies to build a tool capable of storing and processing large volumes of data, which can then be used to analyse trends in sentiment across the UK using the ANEW dataset.
A system is created to gather, store, and analyse messages on Twitter. Throughout the study this system is optimised to handle larger volumes of data, and generally increase performance for sentiment analysis. By moving from single to multi-core processing, the time it takes to perform analysis can be reduced by around 1200%.
Initial analysis of sentiment data shows that maps are an ineffective way to visualise sentiment over time, particularly over a large geographical area. By moving to temporal line graphs significant fluctuations in sentiment data can be shown to occur across the period of a week.
Further analysis has found that there is a regular sentiment heartbeat on Twitter that, when smoothed, highlights a consistent and key trend in sentiment across the period of a week. Statistical analysis of these trends with t tests further prove that there is a significant difference in sentiment on weekends in comparison to weekdays, correlating with existing studies from the US.
Overall, it has been proven that existing Web technologies are capable of this type of analysis. This study has also proven that there are significant trends in sentiment on Twitter, and that these trends can fluctuate during key public events across the UK.