No abstract

This study aims to develop a system that classifies tweets by their subject and implement it as a mobile application. Tweets from user's home timeline are classified into topics taken from the user. Classification is done by using the ontologies of the topics'. Wikipedia pages and TDK definitions of the terms are used while creating the ontologies. Links referencing other Wikipedia pages that found in the terms' Wikipedia page are taken as ontological relations. Strength of these relations are calculated using the Wikipedia descriptions and TDK definitions. Ontology of the topic is created using terms with strong relations. Tweets' relations with ontologies are calculated. Tweets are placed under the topics they are most related to. It is seen that the proposed algorithm is only suitable to classify tweets into maximum of five classes as a result of this study. Three reasons could be observed. First, tweets don't contain enough distinctive words for this algorithm to work. Second, sources chosen to create ontologies don't contain words that are frequently used in daily lives and frequently present in tweets. Third, increasing class count decreases the threshold which tweets' scores has to surpass to be included in a class, therefore causing tweets to be classified into irrelevent classes.