In this paper we present a language processor that solves the task of Morphosyntactic Tagging of messages posted in Portuguese on Twitter. By analyzing the messages written by Brazilian on Twitter, it is easy to notice that new characters are introduced in the alphabet and also that new words are added to the language. Furthermore, we note that these messages are syntactically malformed. This precludes the use of existing Portuguese processors in these messages, nevertheless this problem can be solved by considering these messages as written in a new language, the Portuguese-Twitter. Both the alphabet and the vocabulary of such idiom contain features of Portuguese. However, the grammar is are different. In order to build the processors for this new language, we have used a supervised learning technique known as Entropy Guided Transformation Learning (ETL). Additionally, to train ETL processors, we have built an annotated corpus of messages in Portuguese-Twitter. We are not aware of any other taggers for the Morphosyntactic Portuguese-Twitter task, thus we have compared our tagger to the the accuracy of state-of-art Morphosyntactic Annotation for Portuguese, which has accuracy around 96% depending on the tag set chosen. To assess the quality of the processor, we have used accuracy, which measures how many tokens were tagged correctly. Our experimental results show an accuracy of 90,24% for the proposed Morphosyntatic Tagger. This corresponds to significant learning, since the initial baseline system has an accuracy of only 76,58%. This finding is consistent with the observed learning for the corresponding regular Portuguese taggers.