TY - GEN
T1 - LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification
AU - Htait, Amal
AU - Fournier, Sébastien
AU - Bellot, Patrice
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - https://hal.archives-ouvertes.fr/hal-01771654
UR - https://aclanthology.org/S17-2120/
M3 - Conference publication
SP - 718
EP - 722
BT - International Workshop on Semantic Evaluation
PB - Association for Computational Linguistics
ER -