SiTAKA at SemEval-2017 Task 4: Sentiment Analysis in Twitter Based on a Rich Set of Features
Year: 2017
Type: conference-paper
Abstract: This paper describes SiTAKA, our system that has been used in task 4A, English and Arabic languages, Sentiment Analysis in Twitter of SemEval2017.The system proposes the representation of tweets using a novel set of features, which include a bag of negated words and the information provided by some lexicons.The polarity of tweets is determined by a classifier based on a Support Vector Machine.Our system ranks 2nd among 8 systems in the Arabic language tweets and ranks 8th among 38 systems in the English-language tweets.
DOI: 10.18653/v1/s17-2115
Authors: Mohammed Jabreel, Antonio Moreno
Institution: Universitat Rovira i Virgili
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2750880011