26 research outputs found
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
[multi’vocal]: reflections on engaging everyday people in the development of a collective non-binary synthesized voice
The growing field of Human-Computer Interaction (HCI) takes a step out from conventional screenbased interactions, creating new scenarios, in which voice synthesis and voice recognition become important elements. Such voices are commonly created through concatenative or parametric synthesis methods, which access large voice corpora, pre-recorded by a single professional voice actor. These designed voices arguably propagate representations of gender binary identities. In this paper we present our project, [multi’vocal], which aims to challenge the current gender binary representations in synthesized voices. More specifically we explore if it is possible to create a non-binary synthesized voice through engaging everyday people of diverse backgrounds in giving voice to a collective synthesized voice of all genders, ages and accents