Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets

Zewen Chi, Heyan Huang, Jiangui Chen, Hao Wu, Ran Wei

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a method for Affect in Tweets, which is the task to automatically determine the intensity of emotions and intensity of sentiment of tweets. The term affect refers to emotion-related categories such as anger, fear, etc. Intensity of emotions need to be quantified into a real valued score in [0, 1]. We propose an ensemble system including four different deep learning methods which are CNN, Bidirectional LSTM (BLSTM), LSTM-CNN and a CNN-based Attention model (CA). Our system gets an average Pearson correlation score of 0.682 in the subtask EI-reg and an average Pearson correlation score of 0.784 in subtask V-reg, which ranks 19th among 48 systems in EI-reg and 17th among 38 systems in V-reg.

Original languageEnglish
Title of host publicationNAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop
EditorsMarianna Apidianaki, Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
PublisherAssociation for Computational Linguistics (ACL)
Pages313-318
Number of pages6
ISBN (Electronic)9781948087209
Publication statusPublished - 2018
Event12th International Workshop on Semantic Evaluation, SemEval 2018, co-located with the 16th Annual Conference of the North American Chapter of the - New Orleans, United States
Duration: 5 Jun 20186 Jun 2018

Publication series

NameNAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop

Conference

Conference12th International Workshop on Semantic Evaluation, SemEval 2018, co-located with the 16th Annual Conference of the North American Chapter of the
Country/TerritoryUnited States
CityNew Orleans
Period5/06/186/06/18

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