DUTH at SemEval-2021 Task 7: Is Conventional Machine Learning for Humorous and Offensive Tasks enough in 2021?

no code implementations • SEMEVAL 2021 • Alexandros Karasakalidis, Dimitrios Effrosynidis, Avi Arampatzis

This paper describes the approach that was developed for SemEval 2021 Task 7 (Hahackathon: Incorporating Demographic Factors into Shared Humor Tasks) by the DUTH Team.

Classification regression

DUTH at SemEval-2018 Task 2: Emoji Prediction in Tweets

no code implementations • SEMEVAL 2018 • Dimitrios Effrosynidis, Georgios Peikos, Symeon Symeonidis, Avi Arampatzis

This paper describes the approach that was developed for SemEval 2018 Task 2 (Multilingual Emoji Prediction) by the DUTH Team.

Information Retrieval Task 2

DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis

no code implementations • SEMEVAL 2017 • Symeon Symeonidis, Dimitrios Effrosynidis, John Kordonis, Avi Arampatzis

This report describes our participation to SemEval-2017 Task 4: Sentiment Analysis in Twitter, specifically in subtasks A, B, and C. The approach for text sentiment classification is based on a Majority Vote scheme and combined supervised machine learning methods with classical linguistic resources, including bag-of-words and sentiment lexicon features.

BIG-bench Machine Learning General Classification +3

DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles

no code implementations • SEMEVAL 2017 • Symeon Symeonidis, John Kordonis, Dimitrios Effrosynidis, Avi Arampatzis

We present the system developed by the team DUTH for the participation in Semeval-2017 task 5 - Fine-Grained Sentiment Analysis on Financial Microblogs and News, in subtasks A and B.

BIG-bench Machine Learning Feature Engineering +3