Ecole d'ingénieur et centre de recherche en Sciences du numérique

SentiME++ at SemEval-2017 Task 4A: Stacking state-of-the-art classifiers to enhance sentiment classification

Palumbo, Enrico; Sygkounas, Efstratios; Troncy, Raphaël; Rizzo, Giuseppe

SEMEVAL 2017, 11th International Workshop on Semantic Evaluation, collocated with the 55th annual meeting of the Association for Computational Linguistics (ACL), August 3-4, 2017, Vancouver, Canada

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.

Document Bibtex

Titre:SentiME++ at SemEval-2017 Task 4A: Stacking state-of-the-art classifiers to enhance sentiment classification
Type:Conférence
Langue:English
Ville:Vancouver
Pays:CANADA
Date:
Département:Data Science
Eurecom ref:5237
Copyright: Copyright ACL. Personal use of this material is permitted. The definitive version of this paper was published in SEMEVAL 2017, 11th International Workshop on Semantic Evaluation, collocated with the 55th annual meeting of the Association for Computational Linguistics (ACL), August 3-4, 2017, Vancouver, Canada and is available at :
Bibtex: @inproceedings{EURECOM+5237, year = {2017}, title = {{S}enti{ME}++ at {S}em{E}val-2017 {T}ask 4{A}: {S}tacking state-of-the-art classifiers to enhance sentiment classification}, author = {{P}alumbo, {E}nrico and {S}ygkounas, {E}fstratios and {T}roncy, {R}apha{\"e}l and {R}izzo, {G}iuseppe}, booktitle = {{SEMEVAL} 2017, 11th {I}nternational {W}orkshop on {S}emantic {E}valuation, collocated with the 55th annual meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL}), {A}ugust 3-4, 2017, {V}ancouver, {C}anada}, address = {{V}ancouver, {CANADA}}, month = {08}, url = {http://www.eurecom.fr/publication/5237} }
Voir aussi: