BOUNCE: Sentiment classification in Twitter using rich feature sets

Nadin Kökciyan; Arda Celebi; Arzucan Ozgür; Suzan Usküdarli

BOUNCE: Sentiment classification in Twitter using rich feature sets

Nadin Kökciyan, Arda Celebi, Arzucan Ozgür, Suzan Usküdarli

School of Informatics

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

Abstract

The widespread use of Twitter makes it very interesting to determine the opinions and the sentiments expressed by its users. The shortness of the length and the highly informal nature of tweets render it very difficult to automatically detect such information. This paper reports the results to a challenge, set forth by SemEval-2013 Task 2, to determine the positive, neutral, or negative sentiments of tweets. Two systems are explained: System A for determining the sentiment of a phrase within a tweet and System B for determining the sentiment of a tweet. Both approaches rely on rich feature sets, which are explained in detail.

Original language	English
Title of host publication	Second Joint Conference on Lexical and Computational Semantics (* SEM)
Subtitle of host publication	Volume 2: Proceedings of the Seventh International Workshopon Semantic Evaluation (SemEval 2013)
Publisher	Association for Computational Linguistics (ACL)
Pages	554-561
Number of pages	8
Volume	2
ISBN (Print)	978-1-937284-49-7
Publication status	Published - 15 Jun 2013
Event	Seventh International Workshop on Semantic Evaluation 2013 - Atlanta, United States Duration: 14 Jun 2013 → 15 Jun 2013

Conference

Conference	Seventh International Workshop on Semantic Evaluation 2013
Abbreviated title	SemEval 2013
Country/Territory	United States
City	Atlanta
Period	14/06/13 → 15/06/13

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BOUNCE Sentiment Classification_KOKCIYAN_DOA07112019_VOR_CC-BY-NC-SAFinal published version, 181 KBLicence: Creative Commons: Attribution-NonCommercial-ShareAlike (CC BY-NC-SA)

https://www.aclweb.org/anthology/S13-2093/Licence: Creative Commons: Attribution-NonCommercial-ShareAlike (CC BY-NC-SA)

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Kökciyan, N., Celebi, A., Ozgür, A., & Usküdarli, S. (2013). BOUNCE: Sentiment classification in Twitter using rich feature sets. In Second Joint Conference on Lexical and Computational Semantics (* SEM): Volume 2: Proceedings of the Seventh International Workshopon Semantic Evaluation (SemEval 2013) (Vol. 2, pp. 554-561). Association for Computational Linguistics (ACL). https://www.aclweb.org/anthology/S13-2093/

Kökciyan, Nadin ; Celebi, Arda ; Ozgür, Arzucan et al. / BOUNCE: Sentiment classification in Twitter using rich feature sets. Second Joint Conference on Lexical and Computational Semantics (* SEM): Volume 2: Proceedings of the Seventh International Workshopon Semantic Evaluation (SemEval 2013). Vol. 2 Association for Computational Linguistics (ACL), 2013. pp. 554-561

@inproceedings{2479705b8e9945c9a0b7824b8d33e518,

title = "BOUNCE: Sentiment classification in Twitter using rich feature sets",

abstract = "The widespread use of Twitter makes it very interesting to determine the opinions and the sentiments expressed by its users. The shortness of the length and the highly informal nature of tweets render it very difficult to automatically detect such information. This paper reports the results to a challenge, set forth by SemEval-2013 Task 2, to determine the positive, neutral, or negative sentiments of tweets. Two systems are explained: System A for determining the sentiment of a phrase within a tweet and System B for determining the sentiment of a tweet. Both approaches rely on rich feature sets, which are explained in detail.",

author = "Nadin K{\"o}kciyan and Arda Celebi and Arzucan Ozg{\"u}r and Suzan Usk{\"u}darli",

year = "2013",

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day = "15",

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isbn = "978-1-937284-49-7",

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booktitle = "Second Joint Conference on Lexical and Computational Semantics (* SEM)",

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note = "Seventh International Workshop on Semantic Evaluation 2013, SemEval 2013 ; Conference date: 14-06-2013 Through 15-06-2013",

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Kökciyan, N, Celebi, A, Ozgür, A & Usküdarli, S 2013, BOUNCE: Sentiment classification in Twitter using rich feature sets. in Second Joint Conference on Lexical and Computational Semantics (* SEM): Volume 2: Proceedings of the Seventh International Workshopon Semantic Evaluation (SemEval 2013). vol. 2, Association for Computational Linguistics (ACL), pp. 554-561, Seventh International Workshop on Semantic Evaluation 2013, Atlanta, Georgia, United States, 14/06/13. <https://www.aclweb.org/anthology/S13-2093/>

BOUNCE: Sentiment classification in Twitter using rich feature sets. / Kökciyan, Nadin; Celebi, Arda; Ozgür, Arzucan et al.
Second Joint Conference on Lexical and Computational Semantics (* SEM): Volume 2: Proceedings of the Seventh International Workshopon Semantic Evaluation (SemEval 2013). Vol. 2 Association for Computational Linguistics (ACL), 2013. p. 554-561.

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

TY - GEN

T1 - BOUNCE: Sentiment classification in Twitter using rich feature sets

AU - Kökciyan, Nadin

AU - Celebi, Arda

AU - Ozgür, Arzucan

AU - Usküdarli, Suzan

PY - 2013/6/15

Y1 - 2013/6/15

N2 - The widespread use of Twitter makes it very interesting to determine the opinions and the sentiments expressed by its users. The shortness of the length and the highly informal nature of tweets render it very difficult to automatically detect such information. This paper reports the results to a challenge, set forth by SemEval-2013 Task 2, to determine the positive, neutral, or negative sentiments of tweets. Two systems are explained: System A for determining the sentiment of a phrase within a tweet and System B for determining the sentiment of a tweet. Both approaches rely on rich feature sets, which are explained in detail.

AB - The widespread use of Twitter makes it very interesting to determine the opinions and the sentiments expressed by its users. The shortness of the length and the highly informal nature of tweets render it very difficult to automatically detect such information. This paper reports the results to a challenge, set forth by SemEval-2013 Task 2, to determine the positive, neutral, or negative sentiments of tweets. Two systems are explained: System A for determining the sentiment of a phrase within a tweet and System B for determining the sentiment of a tweet. Both approaches rely on rich feature sets, which are explained in detail.

M3 - Conference contribution

SN - 978-1-937284-49-7

VL - 2

SP - 554

EP - 561

BT - Second Joint Conference on Lexical and Computational Semantics (* SEM)

PB - Association for Computational Linguistics (ACL)

T2 - Seventh International Workshop on Semantic Evaluation 2013

Y2 - 14 June 2013 through 15 June 2013

ER -

BOUNCE: Sentiment classification in Twitter using rich feature sets

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