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

In the last few years thousands of scientific papers have investigated sentiment analysis, several startups that measure opinions on real data have emerged and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which one is better for identifying the polarity (i.e., positive or negative) of a message. Accordingly, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, \textit{as they are used in practice}, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of twenty-four popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of eighteen labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies considerably across datasets. Aiming at boosting the development of this research area, we open the methods' codes and datasets used in this article, deploying them in a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods

Early Stage Conversation Catalysts on Entertainment-Based Web Forums

International audienc

The Deliberative Influence of Comment Section Structure

Online news comment sections are a ubiquitous presence across media outlets. Although research has assessed both the content and effects of these comment spaces, little is known about how the structure of comment spaces influences news users’ online commentary. As democratic theorists argue, the structure of a communicative space can influence its deliberative quality. We conducted two separate experiments to determine how news comment section structure affects online commentary. We found that both comment section structure and the structure of pre-comment information enhanced both the quantity of comment engagement and its deliberative quality