1. Social Media Visualization Amalgamating Social & Factual data to provide a comprehensive outlook on any geo-location

3. Uses Twitter API Utilises data harvesting program from Nick Horne, previous student’s final year project

4. Weather Analysis GUI Linked to previous harvesting program via a shared database Database connection established by selecting ‘Connect to Database’

6. 1. All harvested Tweet geo-coordinates are analysed via Weather.com to check if the Tweet originates from an alert area. 2. If so, the Tweet text is semantically analysed using the AFINN list, to try to systematically determine an overall positive or negative sentiment. 3. The resulting Tweets and alerts are displayed, with a background colour matching that of the Alert colour rating.

7. 1. All harvested Tweet geo-coordinates are analysed via Weather.com to check if the Tweet originates from an alert area. 2. If so, the Tweet text is semantically analysed using the AFINN list, to try to systematically determine an overall positive or negative sentiment. 3. The resulting Tweets and alerts are displayed, with a background colour matching that of the Alert colour rating.

9. Saving / Exporting Tweets of Interest Each Tweet of Interest highlighted to a user by the system can be recorded for future use by clicking ‘Store Information’ and entering the desired filename. Stores saved output as a.txt file

10. Security An audit trail recording every user action is created and updated after every system event. When a file is saved, an MD5SUM hash of the file is recorded and saved to the audit trail. Access to the audit trail itself is password encrypted

11. Future Possibilities Ability to click a button and instantly get connected to a nearby radio station Ability to instantly view recorded historical data for an area – e.g. Annual temperatures, area crime rate etc. In addition to just exporting information to an external format, I will look into the possibility of being able to internally save and load tweets of interest into the system, in order to recreate exactly what a user first experienced Change current semantic analysis method from AFINN list to a more sophisticated solution such as Alchemy or SentiStrength

12. Design Philosophy Where possible I have tried to make the application as modular as possible. For example, the weather alert filtering criteria could be replaced with a ‘crime rate above x’ requirement, or ‘longitude/latitude values within x & y’, provided that the data is available. Similarly, the application could be modified relatively easily to instead access a networked-database of Tweets, allowing multiple application copies to be used as clients. This could be used in scenarios such as a test centre by giving all candidates access to a shared database and having mock-tweet entries being sent to the database, reviewing each candidate’s actions using the audit trail afterwards. The real-time analysis capabilities of the system allow for a user to provide almost any pre-defined filtering criteria, and the system will then find matches/anomalies and highlight them for human- inspection.