With the increasing number of people using micro blogging sites (such as Facebook, Twitter, Youtube, Instagram, Foursquare or Google+), social media data became a highly attractive source for machine learning and natural language processing (NLP) research employed in many high-tier applications including social network analysis, information extraction, web mining, opinion mining and brand monitoring. The language used in social media di ers severely from formally written texts, in that people do not feel forced to write grammatically correct words or sentences. In this talk, we are going to present our current research on Parsing Web2.0 Language and Sentiment Analysis for a morphologically very complex language: Turkish. Short Biography Gulsen Eryigit is an Assistant Professor in the department of Computer Engineering at Istanbul Technical University (ITU), Turkey. She is a founding member of the ITU Natural Language Processing Group and a member of the ITU Learning from Big Data Group. Her current research focuses on natural language processing of Turkish. In this eld, she acted as a reviewer or co-author of various publications in prestigious journals and conferences. She was the Turkeys representative in CLARIN (EU 7th Framework Programme, CLARIN - Common language resources and technology infrastructure). Currently, she is the leader of two ongoing research projects: one EU Cost Action project \Parsing Web2.0 Sentences" funded by the Scienti c and Technological Research Council of Turkey (TUBITAK) and one national project \Turkish Mobile Personal Assistant" funded by Turkeys Ministry of Science, Industry and Technology. She also serves as the NLP coordinator of one interdisciplinary research project \A Signing Avatar System for Turkish to Turkish Sign Language Machine Translation" funded by TUBITAK. She is serving as a consultant or technical advisor to several IT companies in Turkey mostly for brand monitoring and Turkish sentiment analysis projects. She has been a reviewer for many research and industrial proposals for European Commission (H2020 and Cost Action Programs), TUBITAK, Turkey's Ministry of Science, Industry and Technology and Swiss National Science Foundation.