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.

本研究对二十四种正当实践的情感分析方法进行了基准比较，覆盖社交网络、电影、产品评论、新闻文章等十八个数据集，并提供了这些方法的代码和数据集以供使用。结果表明这些方法的预测性能在不同数据集上变化不大。

SentiBench - 现状情感分析方法的基准比较

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

The computation of relatedness between two fragments of text in an automated
manner requires taking into account a wide range of factors pertaining to the
meaning the two fragments convey, and the pairwise relations between their
words. Without doubt, a measure of relatedness between text segments must take
into account both the lexical and the semantic relatedness between words. Such
a measure that captures well both aspects of text relatedness may help in many
tasks, such as text retrieval, classification and clustering. In this paper we
present a new approach for measuring the semantic relatedness between words
based on their implicit semantic links. The approach exploits only a word
thesaurus in order to devise implicit semantic links between words. Based on
this approach, we introduce Omiotis, a new measure of semantic relatedness
between texts which capitalizes on the word-to-word semantic relatedness
measure (SR) and extends it to measure the relatedness between texts. We
gradually validate our method: we first evaluate the performance of the
semantic relatedness measure between individual words, covering word-to-word
similarity and relatedness, synonym identification and word analogy; then, we
proceed with evaluating the performance of our method in measuring text-to-text
semantic relatedness in two tasks, namely sentence-to-sentence similarity and
paraphrase recognition. Experimental evaluation shows that the proposed method
outperforms every lexicon-based method of semantic relatedness in the selected
tasks and the used data sets, and competes well against corpus-based and hybrid
approaches.

本文提出了一种基于词库的、测量文本语义相似性的新方法，称为 Omiotis，它利用了单词之间的语义相关性，将其扩展到文本之间的相关性测量，并在句子相似性和释义识别任务中进行了实验验证，结果显示该方法在选定任务及数据集上表现优异，且与基于语料库和混合方法相媲美，优于所有基于词库的语义相似性方法。