Work

CENTEMENT at SemEval-2018 Task 1: Classification of Tweets using Multiple Thresholds with Self-correction and Weighted Conditional Probabilities

Year: 2018

Type: conference-paper

Abstract: In this paper we present our contribution to SemEval-2018, a classifier for classifying multi-label emotions of Arabic and English tweets.We attempted "Affect in Tweets", specifically Task E-c: Detecting Emotions (multi-label classification).Our method is based on preprocessing the tweets and creating word vectors combined with a self correction step to remove noise.We also make use of emotion specific thresholds.The final submission was selected upon the best performance achieved, selected when using a range of thresholds.Our system was evaluated on the Arabic and English datasets provided for the task by the competition organisers, where it ranked 2nd for the Arabic dataset (out of 14 entries) and 12th for the English dataset (out of 35 entries).

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.18653/v1/s18-1030

Authors: Tariq Ahmad, Allan Ramsay, Hanady Ahmed

Institutions: University of Manchester, Qatar University

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Qatar Foundation, Fonds National de la Recherche Luxembourg, Qatar National Research Fund

Award: G3362826306