ARB-SEN at SemEval-2018 Task1: A New Set of Features for Enhancing the Sentiment Intensity Prediction in Arabic Tweets
Year: 2018
Type: conference-paper
Abstract: This article describes our proposed Arabic Sentiment Analysis system named ARB-SEN.This system is designed for the International Workshop on Semantic Evaluation 2018 (SemEval-2018), Task1: Affect in Tweets.ARB-SEN proposes two supervised models to estimate the sentiment intensity in Arabic tweets.Both models use a set of features including sentiment lexicon, negation, word embedding and emotion symbols features.Our system combines these features to assist the sentiment analysis task.ARB-SEN system achieves a correlation score of 0.720, ranking 6th among all participants in the valence intensity regression (V-reg) for the Arabic sub-task organized within the Se-mEval 2018 evaluation campaign.
DOI: 10.18653/v1/s18-1055
Author: El Moatez Billah Nagoudi
Institution: University of Eloued
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2807683594