ECNU at SemEval-2016 task 7: An enhanced supervised learning method for Lexicon Sentiment Intensity ranking

Feixiang Wang, Zhihua Zhang, Man Lan

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

12 Scopus citations

Abstract

This paper describes our system submissions to task 7 in SemEval 2016, i.e., Determining Sentiment Intensity. We participated the first two subtasks in English, which are to predict the sentiment intensity of a word or a phrase in English Twitter and General English domains. To address this task, we present a supervised learning-to-rank system to predict the relevant scores, i.e., the strength associated with positive sentiment, for English words or phrases. Multiple linguistic and sentiment features are adopted, e.g., Sentiment Lexicons, Sentiment Word Vectors, Word Vectors, Linguistic Features, etc. Officially released results showed that our systems rank the 1st among all submissions in English, which proves the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationSemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages491-496
Number of pages6
ISBN (Electronic)9781941643952
DOIs
StatePublished - 2016
Event10th International Workshop on Semantic Evaluation, SemEval 2016 - San Diego, United States
Duration: 16 Jun 201617 Jun 2016

Publication series

NameSemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings

Conference

Conference10th International Workshop on Semantic Evaluation, SemEval 2016
Country/TerritoryUnited States
CitySan Diego
Period16/06/1617/06/16

Fingerprint

Dive into the research topics of 'ECNU at SemEval-2016 task 7: An enhanced supervised learning method for Lexicon Sentiment Intensity ranking'. Together they form a unique fingerprint.

Cite this