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

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

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.

Detail

Document

DOI

BIBTEX

Type:

Conference

City:

Vancouver

Date:

2017-08-03

Department:

Data Science

Eurecom Ref:

5237

Copyright ACL. Personal use of this material is permitted. The definitive version of this paper was published in and is available at : http://dx.doi.org/10.18653/v1/S17-2107