AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets
- Resource Type
- Authors
- Artur Kulmizev; Mostafa Abdou; Joan Ginés i Ametllé
- Source
- SemEval@NAACL-HLT
- Subject
- Computer science
Arabic
business.industry
02 engineering and technology
computer.software_genre
SemEval
language.human_language
Task (project management)
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
language
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Natural language processing
Cross linguistic
- Language
In this paper we describe our submission to SemEval-2018 Task 1: Affects in Tweets. The model which we present is an ensemble of various neural architectures and gradient boosted trees, and employs three different types of vectorial tweet representations. Furthermore, our system is language-independent and ranked first in 5 out of the 12 subtasks in which we participated, while achieving competitive results in the remaining ones. Comparatively remarkable performance is observed on both the Arabic and Spanish languages.