@inproceedings{htait-etal-2017-lsis,
title = "{LSIS} at {S}em{E}val-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For {E}nglish and {A}rabic Tweet Polarity Classification",
author = "Htait, Amal and
Fournier, S{\'e}bastien and
Bellot, Patrice",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2120",
doi = "10.18653/v1/S17-2120",
pages = "718--722",
abstract = "We present, in this paper, our contribution in SemEval2017 task 4 : {``}Sentiment Analysis in Twitter{''}, subtask A: {``}Message Polarity Classification{''}, for English 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.",
}
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<abstract>We present, in this paper, our contribution in SemEval2017 task 4 : “Sentiment Analysis in Twitter”, subtask A: “Message Polarity Classification”, for English 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.</abstract>
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%0 Conference Proceedings
%T LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification
%A Htait, Amal
%A Fournier, Sébastien
%A Bellot, Patrice
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F htait-etal-2017-lsis
%X We present, in this paper, our contribution in SemEval2017 task 4 : “Sentiment Analysis in Twitter”, subtask A: “Message Polarity Classification”, for English 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.
%R 10.18653/v1/S17-2120
%U https://aclanthology.org/S17-2120
%U https://doi.org/10.18653/v1/S17-2120
%P 718-722
Markdown (Informal)
[LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification](https://aclanthology.org/S17-2120) (Htait et al., SemEval 2017)
ACL