Show simple item record

dc.contributor.authorNgoge, Lucas Achuku
dc.date.accessioned2016-10-07T15:26:46Z
dc.date.available2016-10-07T15:26:46Z
dc.date.issued2016
dc.identifier.urihttp://hdl.handle.net/11071/4826
dc.descriptionA research thesis submitted in partial fulfillment of the requirements of the Degree of Master of Science in Information Technologyen_US
dc.description.abstractTerrorism 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%.en_US
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectTerrorismen_US
dc.subjectSentiment analysisen_US
dc.subjectDetectionen_US
dc.subjectTerrorism detectionen_US
dc.titleReal – time sentiment analysis for detection of terrorist activities in Kenyaen_US
dc.typeThesisen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record