Sentiment analysis is the task of determining the opinion expressed on subjective data, which may include microblog messages, such as tweets. This type of message has been considered the target of sentiment analysis in many recent studies, since they represent a rich source of opinionated texts. Thus, in order to determine the opinion expressed in tweets, different studies have employed distinct strategies, which mainly include supervised machine learning methods. For this purpose, different kinds of features have been evaluated. Despite that, none of the state-of-the-art studies has evaluated distinct categories of features, regarding their similar characteristics. In this context, this work presents a literature review of the most common feature representation in Twitter sentiment analysis. We propose to group features sharing similar aspects into specific categories. We also evaluate the relevance of these categories of features, including meta-level features, using a significant number of Twitter datasets. Furthermore, we apply important and well-known feature selection strategies in order to identify relevant subsets of features for each dataset. We show in the experimental evaluation that the results achieved in this study, using feature selection strategies, outperform the results reported in previous works for the most of the assessed datasets.