Sentiment analysis from text consists of extracting information about opinions, sentiments,
and even emotions conveyed by writers towards topics of interest. It is often equated to …

Media bias, ie, slanted news coverage, can strongly impact the public perception of the
reported topics. In the social sciences, research over the past decades has developed …

A multi-label classification based approach for sentiment analysis is proposed in this paper.
To the best of our knowledge, this work is the first to propose to use multi-label classification …

Multi‐label learning is quite a recent supervised learning paradigm. Owing to its capabilities
to improve performance in problems where a pattern may have more than one associated …

Sentiment analysis has become a key research area in natural language processing due to
its wide range of practical applications that include opinion mining, emotions extraction …

The rapid development of social media services has facilitated the communication of
opinions through online news, blogs, microblogs/tweets, instant-messages, and so forth …

In this paper, we focus on the task of multi-label emotion classification and aim to tackle two
problems of this task. First, few studies try to exploit the correlation among different emotions …

With the accelerated evolution of the internet in the form of web-sites, social networks,
microblogs, and online portals, a large number of reviews, opinions, recommendations …

Sentiment Analysis is an emerging research field traditionally applied to classify opinions,
sentiments and emotions towards polarity and subjectivity expressed in text. An important …

The main task we address in our research is classification of text using fine-grained attitude
labels. The developed@ AM system relies on the compositionality principle and a novel …