SemEval-2015 Task 9: CLIPEval Implicit Polarity of Events

ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter

This paper describes the system we have used for participating in Subtasks A (Message Polarity Classification) and B (Topic-Based Message Polarity Classification according to a two-point scale) of SemEval-2017 Task 4 Sen…

NTUA-ISLab at SemEval-2019 Task 3: Determining emotions in contextual conversations with deep learning

Sentiment analysis (SA) in texts is a well-studied Natural Language Processing task, which in nowadays gains popularity due to the explosion of social media, and the subsequent accumulation of huge amounts of related dat…

ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification

This paper reports our submission to subtask A of task 4 (Sentiment Analysis in Twitter, SAT) in SemEval 2017, i.e., Message Polarity Classification. We investigated several traditional Natural Language Processing (NLP) …

DCU: Aspect-based Polarity Classification for SemEval Task 4

LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification

We present, in this paper, our contribution in SemEval2017 task 4 : {``}Sentiment Analysis in Twitter{''}, subtask A: {``}Message Polarity Classification{''}, for English and Arabic languages. Our system is based on a li…