TY - GEN
T1 - CICBUAPnlp at SemEval-2016 task 4-a
T2 - 10th International Workshop on Semantic Evaluation, SemEval 2016
AU - Gómez-Adorno, Helena
AU - Sidorov, Grigori
AU - Vilariño, Darnes
AU - Pinto, David
N1 - Publisher Copyright:
© 2016 Association for Computational Linguistics.
PY - 2016
Y1 - 2016
N2 - This paper presents our approach for SemEval 2016 task 4: Sentiment Analysis in Twitter. We participated in Subtask A: Message Polarity Classification. The aim is to classify Twitter messages into positive, neutral, and negative polarity. We used a lexical resource for pre-processing of social media data and train a neural network model for feature representation. Our resource includes dictionaries of slang words, contractions, abbreviations, and emoticons commonly used in social media. For the classification process, we pass the features obtained in an unsupervised manner into an SVM classifier.
AB - This paper presents our approach for SemEval 2016 task 4: Sentiment Analysis in Twitter. We participated in Subtask A: Message Polarity Classification. The aim is to classify Twitter messages into positive, neutral, and negative polarity. We used a lexical resource for pre-processing of social media data and train a neural network model for feature representation. Our resource includes dictionaries of slang words, contractions, abbreviations, and emoticons commonly used in social media. For the classification process, we pass the features obtained in an unsupervised manner into an SVM classifier.
UR - http://www.scopus.com/inward/record.url?scp=85035757315&partnerID=8YFLogxK
M3 - Contribución a la conferencia
AN - SCOPUS:85035757315
T3 - SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings
SP - 145
EP - 148
BT - SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings
PB - Association for Computational Linguistics (ACL)
Y2 - 16 June 2016 through 17 June 2016
ER -