LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification

Amal Htait, Sébastien Fournier, Patrice Bellot

Research output: Chapter in Book/Published conference outputConference publication

Abstract

We present, in this paper, our contribution in SemEval2017 task 4 : " Sentiment Analysis in Twitter " , subtask A: " Message Polarity Classification " , for En-glish and Arabic languages. Our system is based on a list of sentiment seed words adapted for tweets. The sentiment relations between seed words and other terms are captured by cosine similarity between the word embedding representations (word2vec). These seed words are extracted from datasets of annotated tweets available online. Our tests, using these seed words, show significant improvement in results compared to the use of Turney and Littman's (2003) seed words, on polarity classification of tweet messages.
Original languageEnglish
Title of host publicationInternational Workshop on Semantic Evaluation
PublisherAssociation for Computational Linguistics
Pages718-722
Number of pages5
Publication statusPublished - 2017

Fingerprint

Dive into the research topics of 'LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification'. Together they form a unique fingerprint.

Cite this