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In the last few years thousands of scientific papers have investigatedsentiment analysis, several startups that measure opinions on real data haveemerged and a number of innovative products related to this theme have beendeveloped. There are multiple methods for measuring sentiments, includinglexical-based and supervised machine learning methods. Despite the vastinterest on the theme and wide popularity of some methods, it is unclear whichone is better for identifying the polarity (i.e., positive or negative) of amessage. Accordingly, there is a strong need to conduct a thoroughapple-to-apple comparison of sentiment analysis methods, \textit{as they areused in practice}, across multiple datasets originated from different datasources. Such a comparison is key for understanding the potential limitations,advantages, and disadvantages of popular methods. This article aims at fillingthis gap by presenting a benchmark comparison of twenty-four popular sentimentanalysis methods (which we call the state-of-the-practice methods). Ourevaluation is based on a benchmark of eighteen labeled datasets, coveringmessages posted on social networks, movie and product reviews, as well asopinions and comments in news articles. Our results highlight the extent towhich the prediction performance of these methods varies considerably acrossdatasets. Aiming at boosting the development of this research area, we open themethods' codes and datasets used in this article, deploying them in a benchmarksystem, which provides an open API for accessing and comparing sentence-levelsentiment analysis methods.

SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods