SemEval 2014 Task 9

We report the results we obtained at the sub- task B (Message Polarity Classification) of Se- mEval 2014 Task 9. The features used for representing the messages were basically tri- …...

This paper describes our contribution to the SemEval-2014 Task 9 on sentiment analysis in Twitter. We participated in both strands of the task, viz. classification at message-level (subtask B), and …...

This paper proposes an approach to solve message- and phrase-level polarity classi- fication in Twitter, derived from an exist- ing system designed for Spanish. As a first step, an ad-hoc …...

This paper describes the NILC USP sys- tem that participated in SemEval-2014 Task 9: Sentiment Analysis in Twitter, a re-run of the SemEval 2013 task under the same name. Our …...

This paper describes state-of-the-art statis- tical systems for automatic sentiment anal- ysis of tweets. In a Semeval-2014 shared task (Task 9), our submissions obtained highest scores in the term-level sentiment …...

This paper describes our submission to Se- mEval2014 Task 9: Sentiment Analysis in Twitter. Our model is primarily a lexi- con based one, augmented by some pre- processing, including detection …...

This document describes the senti.ue system and how it was used for partici- pation in SemEval-2014 Task 9 challenge. Our system is an evolution of our prior work, also used …...

In this paper we describe a Deep Convo- lutional Neural Network (DNN) approach to perform two sentiment detection tasks: message polarity classification and con- textual polarity disambiguation. We apply the …...