@inproceedings{troncy-etal-2017-sentime,
title = "{S}enti{ME}++ at {S}em{E}val-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification",
author = {Troncy, Rapha\"el and
Palumbo, Enrico and
Sygkounas, Efstratios and
Rizzo, Giuseppe},
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2107/",
doi = "10.18653/v1/S17-2107",
pages = "648--652",
abstract = "In this paper, we describe the participation of the SentiME++ system to the SemEval 2017 Task 4A ``Sentiment Analysis in Twitter'' that aims to classify whether English tweets are of positive, neutral or negative sentiment. SentiME++ is an ensemble approach to sentiment analysis that leverages stacked generalization to automatically combine the predictions of five state-of-the-art sentiment classifiers. SentiME++ achieved officially 61.30\% F1-score, ranking 12th out of 38 participants."
}
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<abstract>In this paper, we describe the participation of the SentiME++ system to the SemEval 2017 Task 4A “Sentiment Analysis in Twitter” that aims to classify whether English tweets are of positive, neutral or negative sentiment. SentiME++ is an ensemble approach to sentiment analysis that leverages stacked generalization to automatically combine the predictions of five state-of-the-art sentiment classifiers. SentiME++ achieved officially 61.30% F1-score, ranking 12th out of 38 participants.</abstract>
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%0 Conference Proceedings
%T SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification
%A Troncy, Raphaël
%A Palumbo, Enrico
%A Sygkounas, Efstratios
%A Rizzo, Giuseppe
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F troncy-etal-2017-sentime
%X In this paper, we describe the participation of the SentiME++ system to the SemEval 2017 Task 4A “Sentiment Analysis in Twitter” that aims to classify whether English tweets are of positive, neutral or negative sentiment. SentiME++ is an ensemble approach to sentiment analysis that leverages stacked generalization to automatically combine the predictions of five state-of-the-art sentiment classifiers. SentiME++ achieved officially 61.30% F1-score, ranking 12th out of 38 participants.
%R 10.18653/v1/S17-2107
%U https://aclanthology.org/S17-2107/
%U https://doi.org/10.18653/v1/S17-2107
%P 648-652
Markdown (Informal)
[SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification](https://aclanthology.org/S17-2107/) (Troncy et al., SemEval 2017)
ACL