With the advent of Internet, people actively express their opinions about products, services,
events, political parties, etc., in social media, blogs, and website comments. The amount of …

The ability to analyse online user-generated content related to sentiments (eg, thoughts and
opinions) on products or policies has become a de-facto skillset for many companies and …

This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis
(ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task …

In this paper, we present a dependency treebased method for sentiment classification of
Japanese and English subjective sentences using conditional random fields with hidden …

While a number of isolated studies have analysed how different sentence features are
beneficial in Sentiment Analysis, a complete picture of their effectiveness is still lacking. In …

ABSTRACT Cross-Lingual Sentiment Analysis (CLSA) is the task of predicting the polarity of
the opinion expressed in a text in a language Ltest using a classifier trained on the corpus of …

The amount of labeled sentiment data in English is much larger than that in other languages.
Such a disproportion arouse interest in cross-lingual sentiment classification, which aims to …

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task designed
to predict the sentiment polarity of each aspect term in a text. Recent research mainly uses …

Most previous work on multilingual sentiment analysis has focused on methods to adapt
sentiment resources from resource-rich languages to resource-poor languages. We present …

Traditional supervised learning algorithms assume that the training data and the test data
are drawn from the same distribution. Models that are purely trained from training data are …