Stance Classification of Tweets Using Skip Char Ngrams

Yaakov HaCohen-Kerner; Ziv Ido; Ronen Ya’akobov

doi:10.1007/978-3-319-71273-4_22

Stance Classification of Tweets Using Skip Char Ngrams

Yaakov HaCohen-Kerner, Ziv Ido, Ronen Ya’akobov

Jerusalem College of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

25 Scopus citations

Abstract

In this research, we focus on automatic supervised stance classification of tweets. Given test datasets of tweets from five various topics, we try to classify the stance of the tweet authors as either in FAVOR of the target, AGAINST it, or NONE. We apply eight variants of seven supervised machine learning methods and three filtering methods using the WEKA platform. The macro-average results obtained by our algorithm are significantly better than the state-of-art results reported by the best macro-average results achieved in the SemEval 2016 Task 6-A for all the five released datasets. In contrast to the competitors of the SemEval 2016 Task 6-A, who did not use any char skip ngrams but rather used thousands of ngrams and hundreds of word embedding features, our algorithm uses a few tens of features mainly character-based features where most of them are skip char ngram features.

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings
Editors	Michelangelo Ceci, Saso Dzeroski, Donato Malerba, Yasemin Altun, Kamalika Das, Jesse Read, Marinka Zitnik, Jerzy Stefanowski, Taneli Mielikäinen
Publisher	Springer Verlag
Pages	266-278
Number of pages	13
ISBN (Print)	9783319712727
DOIs	https://doi.org/10.1007/978-3-319-71273-4_22
State	Published - 2017
Externally published	Yes
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017 - Skopje, Macedonia, The Former Yugoslav Republic of Duration: 18 Sep 2017 → 22 Sep 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10536 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017
Country/Territory	Macedonia, The Former Yugoslav Republic of
City	Skopje
Period	18/09/17 → 22/09/17

Bibliographical note

Publisher Copyright:
© 2017, Springer International Publishing AG.

Keywords

Short texts
Skip character ngrams
Skip word ngrams
Social data
Stance classification
Supervised machine learning
Tweets

Access to Document

10.1007/978-3-319-71273-4_22

Cite this

HaCohen-Kerner, Y., Ido, Z., & Ya’akobov, R. (2017). Stance Classification of Tweets Using Skip Char Ngrams. In M. Ceci, S. Dzeroski, D. Malerba, Y. Altun, K. Das, J. Read, M. Zitnik, J. Stefanowski, & T. Mielikäinen (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings (pp. 266-278). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10536 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-71273-4_22

HaCohen-Kerner, Yaakov ; Ido, Ziv ; Ya’akobov, Ronen. / Stance Classification of Tweets Using Skip Char Ngrams. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. editor / Michelangelo Ceci ; Saso Dzeroski ; Donato Malerba ; Yasemin Altun ; Kamalika Das ; Jesse Read ; Marinka Zitnik ; Jerzy Stefanowski ; Taneli Mielikäinen. Springer Verlag, 2017. pp. 266-278 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this research, we focus on automatic supervised stance classification of tweets. Given test datasets of tweets from five various topics, we try to classify the stance of the tweet authors as either in FAVOR of the target, AGAINST it, or NONE. We apply eight variants of seven supervised machine learning methods and three filtering methods using the WEKA platform. The macro-average results obtained by our algorithm are significantly better than the state-of-art results reported by the best macro-average results achieved in the SemEval 2016 Task 6-A for all the five released datasets. In contrast to the competitors of the SemEval 2016 Task 6-A, who did not use any char skip ngrams but rather used thousands of ngrams and hundreds of word embedding features, our algorithm uses a few tens of features mainly character-based features where most of them are skip char ngram features.",

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author = "Yaakov HaCohen-Kerner and Ziv Ido and Ronen Ya{\textquoteright}akobov",

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HaCohen-Kerner, Y, Ido, Z & Ya’akobov, R 2017, Stance Classification of Tweets Using Skip Char Ngrams. in M Ceci, S Dzeroski, D Malerba, Y Altun, K Das, J Read, M Zitnik, J Stefanowski & T Mielikäinen (eds), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10536 LNAI, Springer Verlag, pp. 266-278, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017, Skopje, Macedonia, The Former Yugoslav Republic of, 18/09/17. https://doi.org/10.1007/978-3-319-71273-4_22

Stance Classification of Tweets Using Skip Char Ngrams. / HaCohen-Kerner, Yaakov; Ido, Ziv; Ya’akobov, Ronen.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. ed. / Michelangelo Ceci; Saso Dzeroski; Donato Malerba; Yasemin Altun; Kamalika Das; Jesse Read; Marinka Zitnik; Jerzy Stefanowski; Taneli Mielikäinen. Springer Verlag, 2017. p. 266-278 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10536 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Ido, Ziv

AU - Ya’akobov, Ronen

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N2 - In this research, we focus on automatic supervised stance classification of tweets. Given test datasets of tweets from five various topics, we try to classify the stance of the tweet authors as either in FAVOR of the target, AGAINST it, or NONE. We apply eight variants of seven supervised machine learning methods and three filtering methods using the WEKA platform. The macro-average results obtained by our algorithm are significantly better than the state-of-art results reported by the best macro-average results achieved in the SemEval 2016 Task 6-A for all the five released datasets. In contrast to the competitors of the SemEval 2016 Task 6-A, who did not use any char skip ngrams but rather used thousands of ngrams and hundreds of word embedding features, our algorithm uses a few tens of features mainly character-based features where most of them are skip char ngram features.

AB - In this research, we focus on automatic supervised stance classification of tweets. Given test datasets of tweets from five various topics, we try to classify the stance of the tweet authors as either in FAVOR of the target, AGAINST it, or NONE. We apply eight variants of seven supervised machine learning methods and three filtering methods using the WEKA platform. The macro-average results obtained by our algorithm are significantly better than the state-of-art results reported by the best macro-average results achieved in the SemEval 2016 Task 6-A for all the five released datasets. In contrast to the competitors of the SemEval 2016 Task 6-A, who did not use any char skip ngrams but rather used thousands of ngrams and hundreds of word embedding features, our algorithm uses a few tens of features mainly character-based features where most of them are skip char ngram features.

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KW - Stance classification

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HaCohen-Kerner Y, Ido Z, Ya’akobov R. Stance Classification of Tweets Using Skip Char Ngrams. In Ceci M, Dzeroski S, Malerba D, Altun Y, Das K, Read J, Zitnik M, Stefanowski J, Mielikäinen T, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. Springer Verlag. 2017. p. 266-278. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-71273-4_22

Stance Classification of Tweets Using Skip Char Ngrams

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Keywords

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