Real – time sentiment analysis for detection of terrorist activities in Kenya
Ngoge, Lucas Achuku
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Terrorism has become a subject of concern to many people in Kenya today. Majority of people are worried lot because they don’t know when they will become victims of terrorists’ activities. Corruption, porous border and luck of government in the neighboring Somali, have made Kenya a potential target for terrorists’. The advancement in technology has brought a new era in terrorism where Online Social Networks such as Twitter, Facebook has driven the increase use of the internet by terrorist organizations and their supporters for a wide range of purposes including recruitment, financing, propaganda, incitement to commit acts of terrorism and the gathering and dissemination of information for terrorist activities. Although the Kenya government improved its ability to fight terrorism but the changing pattern of terrorist activities, human errors and delayed crime analyses have given criminals more time to destroy evidence and escape arrest. The evolution of computerized systems has made tracking of terrorist’ activities easier. This has helped the law enforcement officers to speed up the process of solving crimes. In this research data was collected from twitter then followed by sentiment analysis on tweets collected to derive rules for the real-time classifier. Geographic analysis was done to reveal a correlation between the tweets and the terrorist’ activities as portrayed by the map. The main objective of this research is to develop a model that will be used to establish crime patterns associated with terrorist activities using sentiment information deduced from twitter data. To achieve this objective, 346 tweets related to terrorism were collected, cleansed and stored in a database for a period of 7 days. This data was then used as features for training and development of the model which will then be used to carry out real time sentiment analysis on twitter data. The model was tested and it was able to classify text correctly into positive, negative and neutral classes with an accuracy score of 73%.