With the widespread usage of social media in our daily lives, user reviews emerged as an impactful factor for numerous fields including understanding consumer attitudes, determining political tendency, revealing strengths or weaknesses of many different organizations. Today, people are chatting with their friends, carrying out social relations, shopping and following many current events through the social media. However social media limits the size of user messages. The users generally express their opinions by using emoticons, abbreviations, slangs, and symbols instead of words. This situation makes the sentiment classification of social media texts more complex. In this paper a sentiment classification model for Twitter messages is proposed to overcome this difficulty. In the proposed model first the short messages are expanded with BabelNet which is a concept network. Then the expanded and the original form of the messages are included in an ensemble learning model. Consequently we compared our ensemble model with traditional classification algorithms and observed that the F-measure value is increased.

Today Wikipedia provides a very large and reliable domain-independent encyclopedic repository. With this study a mobile system which summarizes Turkish Wikipedia text is presented. The presented system selects the sentences due to structural features of Turkish language and semantic features of the sentences. The performance evaluation is made based on judgments of human experts. The results are tested due to precision and recall values of a ranked sentence list and it is concluded that, the summarization results are promising.