IHS-RD-Belarus at SemEval-2016 Task 5: Detecting Sentiment Polarity Using the Heatmap of Sentence

INF-UFRGS at SemEval-2017 Task 5: A Supervised Identification of Sentiment Score in Tweets and Headlines

This paper describes a supervised solution for detecting the polarity scores of tweets or headline news in the financial domain, submitted to the SemEval 2017 Fine-Grained Sentiment Analysis on Financial Microblogs and N…

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…

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…

UCSC-NLP at SemEval-2017 Task 4: Sense n-grams for Sentiment Analysis in Twitter

This paper describes the system submitted to SemEval-2017 Task 4-A Sentiment Analysis in Twitter developed by the UCSC-NLP team. We studied how relationships between sense n-grams and sentiment polarities can contribute …

SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis