SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification

no code implementations • SEMEVAL 2017 • Rapha{\"e}l Troncy, Enrico Palumbo, Efstratios Sygkounas, Giuseppe Rizzo

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.

General Classification Sentiment Analysis +1