NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter Sentiment Analysis
This is an incremental improvement for researchers in natural language processing, specifically for Twitter sentiment analysis tasks.
The paper tackled sentiment analysis on Twitter by developing a multi-view ensemble system for the SemEval-2017 Task 4, achieving 18th place out of 38 systems in F1 score and 20th in recall.
This paper describes our multi-view ensemble approach to SemEval-2017 Task 4 on Sentiment Analysis in Twitter, specifically, the Message Polarity Classification subtask for English (subtask A). Our system is a voting ensemble, where each base classifier is trained in a different feature space. The first space is a bag-of-words model and has a Linear SVM as base classifier. The second and third spaces are two different strategies of combining word embeddings to represent sentences and use a Linear SVM and a Logistic Regressor as base classifiers. The proposed system was ranked 18th out of 38 systems considering F1 score and 20th considering recall.