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This paper, seeks to examine the correlation between stock price and public sentiment expressed through social media. Through twitter scraping and pre- processing, sentiment can be extracted from text. The paper will be based on a heuristic approach to natural language processing. Furthermore, the paper will rely on the most common forms of sentiment analysis, using a rule-based and a machine-learning approach as a starting point and weigh these up against each other. Finally, we will continue with the best performing method, and weigh this up against real market data in a pursuit to find a correlation, should one exist. The paper found a sentiment-to-market accuracy 75%. And the accuracy score utilizing the rules-based approach of 72,72%.

This project will deep into the motives and reasons behind cultural destruction. It will underline the will to entirely wipe off a distinctive group of population and its culture. Taking Syria as our main case given the events of the last decade; centring in the civil war where lives and art have been lost forever, together with the morale of its population, which fled to other countries in big numbers throughout the war. As for the perpetrators, ISIS will be the focus of our project, guilty of intentionally destroying many monuments, especially in Syria. Before explaining and answering to the questions of the project regarding culture, it is more than mandatory to analyse Syria’s history, tribes and regions. After reaching the core of our project and describing the possible reasons behind these actions, the third part of our project, which focuses on the emotional sphere of the victims. War brings with itself grief, loss, damage and destruction to a country and its material environment. To focus on the emotional attachment and emotional damage during this war shall be a matter of discussion nevertheless. We will not only analyse and discuss cultural destruction and the loss for humanity whenever perpetrators decide to damage forever something so important as our, as humans, common and singular past.