This paper describes our contribution to the SemEval-2015 Task 11 on sentiment analysis of figurative language in Twitter. We considered two approaches, classification and regression, to provide fine-grained sentiment scores for a set of tweets that are rich in sarcasm, irony and metaphor. To this end, we combined a variety of standard lexical and syntactic features with specific features for capturing figurative content. All experiments were done using supervised learning with LIBSVM. For both runs, our system ranked fourth among fifteen submissions
In this paper, we describe the system we built for Task 11 of SemEval2015, which aims at i-dentifyin...
This paper describes the system we submitted to SemEval-2018 Task 3. The aim of the system is to dis...
Uno de los retos más complejos a los que se enfrenta el Procesamiento de Lenguaje Natural es el de d...
This paper describes our contribution to the SemEval-2015 Task 11 on sentiment analysis of figurativ...
This paper describes our contribution to the SemEval-2015 Task 11 on sentiment analysis of figurativ...
This paper analyzes the problem of figurative language detection on social media, with a focus on th...
In this paper, we propose a new statistical method for sentiment analysis of figurative language wit...
This report summarizes the objectives and evaluation of the SemEval 2015 task on the sentiment analy...
This is the author’s version of a work that was accepted for publication in Knowledge-Based Systems....
Sentiment Analysis of tweets is a complex task, because these short messages employ unconventional l...
The use of irony and sarcasm has been proven to be a pervasive phenomenon in social media posing a c...
This paper describes the system used by the ValenTo team in the Task 11, Sentiment Analysis of Figur...
International audienceThis paper analyzes the problem of figurative language detection on social med...
International Workshop on Semantic Evaluation. WLV at SemEval-2018 Task 3.This paper describes the s...
This paper presents the first shared task on irony detection: given a tweet, automatic natural langu...
In this paper, we describe the system we built for Task 11 of SemEval2015, which aims at i-dentifyin...
This paper describes the system we submitted to SemEval-2018 Task 3. The aim of the system is to dis...
Uno de los retos más complejos a los que se enfrenta el Procesamiento de Lenguaje Natural es el de d...
This paper describes our contribution to the SemEval-2015 Task 11 on sentiment analysis of figurativ...
This paper describes our contribution to the SemEval-2015 Task 11 on sentiment analysis of figurativ...
This paper analyzes the problem of figurative language detection on social media, with a focus on th...
In this paper, we propose a new statistical method for sentiment analysis of figurative language wit...
This report summarizes the objectives and evaluation of the SemEval 2015 task on the sentiment analy...
This is the author’s version of a work that was accepted for publication in Knowledge-Based Systems....
Sentiment Analysis of tweets is a complex task, because these short messages employ unconventional l...
The use of irony and sarcasm has been proven to be a pervasive phenomenon in social media posing a c...
This paper describes the system used by the ValenTo team in the Task 11, Sentiment Analysis of Figur...
International audienceThis paper analyzes the problem of figurative language detection on social med...
International Workshop on Semantic Evaluation. WLV at SemEval-2018 Task 3.This paper describes the s...
This paper presents the first shared task on irony detection: given a tweet, automatic natural langu...
In this paper, we describe the system we built for Task 11 of SemEval2015, which aims at i-dentifyin...
This paper describes the system we submitted to SemEval-2018 Task 3. The aim of the system is to dis...
Uno de los retos más complejos a los que se enfrenta el Procesamiento de Lenguaje Natural es el de d...