Due to the ever-increasing amount of online information, manual processing of data is impractical. Social media such as Twitter play an important role in storing such information and helping people share their ideas. Extracting the attitude and opinion of people from user entered data is worthwhile for companies. Sentiment analysis attempts to extract the embedded polarity from a segment of text (or other data types) with many commercial and con-commercial applications. Companies are interested in opinions of their customers. On the other hand, customers are interested in opinions of other customers. Politicians and policy makers are also interested in public's feedback on political events. The above mentioned opinions can be (semi)automatically extracted from social media such as Twitter or Facebook by the help of sentiment analysis techniques. Sentiment analysis is a language (e.g. English) dependent task that relies on natural language processing techniques. The richest language in terms of resources and research in sentiment analysis is English, while many other languages such as Turkish su er from a lack of resources and techniques for sentiment analysis. In this thesis, we try to ll this gap by designing and implementing a framework for sentiment analysis in Turkish. This framework can also be adapted to other languages with some minor changes. In the scope of the framework, we have built a few Turkish polarity lexicons for the rst time in the literature. We also comprehensively investigated the problem of sentiment analysis in Turkish and suggested some solutions. Experimental evaluation shows the e ectiveness of the proposed resources and techniques for Turkish
