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Sentiment analysis of Twitter data

Authors:

Apoorv Agarwal,

Rebecca PassonneauAuthors Info & Claims

LSM '11: Proceedings of the Workshop on Languages in Social Media

Pages 30 - 38

Published: 23 June 2011 Publication History

Abstract

We examine sentiment analysis on Twitter data. The contributions of this paper are: (1) We introduce POS-specific prior polarity features. (2) We explore the use of a tree kernel to obviate the need for tedious feature engineering. The new features (in conjunction with previously proposed features) and the tree kernel perform approximately at the same level, both outperforming the state-of-the-art baseline.

References

[1]

Apoorv Agarwal, Fadi Biadsy, and Kathleen Mckeown. 2009. Contextual phrase-level polarity analysis using lexical affect scoring and syntactic n-grams. Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pages 24--32, March.

Digital Library

[2]

Luciano Barbosa and Junlan Feng. 2010. Robust sentiment detection on twitter from biased and noisy data. Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pages 36--44.

Digital Library

[3]

Adam Bermingham and Alan Smeaton. 2010. Classifying sentiment in microblogs: is brevity an advantage is brevity an advantage? ACM, pages 1833--1836.

Digital Library

[4]

C. Fellbaum. 1998. Wordnet, an electronic lexical database. MIT Press.

[5]

Michael Gamon. 2004. Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. Proceedings of the 20th international conference on Computational Linguistics.

Digital Library

[6]

Alec Go, Richa Bhayani, and Lei Huang. 2009. Twitter sentiment classification using distant supervision. Technical report, Stanford.

[7]

David Haussler. 1999. Convolution kernels on discrete structures. Technical report, University of California at Santa Cruz.

[8]

M Hu and B Liu. 2004. Mining and summarizing customer reviews. KDD.

Digital Library

[9]

S M Kim and E Hovy. 2004. Determining the sentiment of opinions. Coling.

Digital Library

[10]

Dan Klein and Christopher D. Manning. 2003. Accurate unlexicalized parsing. Proceedings of the 41st Meeting of the Association for Computational Linguistics, pages 423--430.

Digital Library

[11]

Alessandro Moschitti. 2006. Efficient convolution kernels for dependency and constituent syntactic trees. In Proceedings of the 17th European Conference on Machine Learning.

Digital Library

[12]

Alexander Pak and Patrick Paroubek. 2010. Twitter as a corpus for sentiment analysis and opinion mining. Proceedings of LREC.

[13]

B. Pang and L. Lee. 2004. A sentimental education: Sentiment analysis using subjectivity analysis using subjectivity summarization based on minimum cuts. ACL.

Digital Library

[14]

P. Turney. 2002. Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. ACL.

Digital Library

[15]

C M Whissel. 1989. The dictionary of Affect in Language. Emotion: theory research and experience, Acad press London.

[16]

T. Wilson, J. Wiebe, and P. Hoffman. 2005. Recognizing contextual polarity in phrase level sentiment analysis. ACL.

Digital Library

Cited By

Yeh MMetevier BHoag AThomas P(2024)Analyzing the Relationship Between Difference and Ratio-Based Fairness MetricsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658922(518-528)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658922
Luu TFollmann R(2023)The relationship between sentiment score and COVID-19 cases in the United StatesJournal of Information Science10.1177/0165551521106816749:6(1615-1630)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1177/01655515211068167
Hassan SAljohani NTarar USafder ISarwar RAlelyani SNawaz R(2023)Exploiting tweet sentiments in altmetrics large-scale dataJournal of Information Science10.1177/0165551521104371349:5(1229-1245)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1177/01655515211043713
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Sentiment analysis of Twitter data

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Information & Contributors

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Published In

cover image DL Hosted proceedings

LSM '11: Proceedings of the Workshop on Languages in Social Media

June 2011

105 pages

ISBN:9781932432961

Program Chairs:
Meena Nagarajan
IBM Research
,
Michael Gamon
Microsoft Research

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 23 June 2011

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Cited By

Yeh MMetevier BHoag AThomas P(2024)Analyzing the Relationship Between Difference and Ratio-Based Fairness MetricsProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658922(518-528)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658922
Luu TFollmann R(2023)The relationship between sentiment score and COVID-19 cases in the United StatesJournal of Information Science10.1177/0165551521106816749:6(1615-1630)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1177/01655515211068167
Hassan SAljohani NTarar USafder ISarwar RAlelyani SNawaz R(2023)Exploiting tweet sentiments in altmetrics large-scale dataJournal of Information Science10.1177/0165551521104371349:5(1229-1245)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1177/01655515211043713
Perti ASinha AVidyarthi A(2023)Cognitive Hybrid Deep Learning-based Multi-modal Sentiment Analysis for Online Product ReviewsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/361535623:8(1-14)Online publication date: 11-Aug-2023
https://dl.acm.org/doi/10.1145/3615356
Bansal AJain ATomar S(2022)Aspect-Based Sentiment Analysis of Online Reviews for Business IntelligenceInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.30702915:3(1-21)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.4018/IJITSA.307029
Jain SMalik S(2022)Knowledge-Infused Text Classification for the Biomedical DomainInternational Journal of Information System Modeling and Design10.4018/IJISMD.30663513:10(1-15)Online publication date: 16-Sep-2022
https://dl.acm.org/doi/10.4018/IJISMD.306635
Sahi G(2022)Public Sentiment on Ayodhya Verdict by the Supreme Court of IndiaInternational Journal of Information Communication Technologies and Human Development10.4018/IJICTHD.29556114:1(1-17)Online publication date: 23-Feb-2022
https://dl.acm.org/doi/10.4018/IJICTHD.295561
Martinis MZucco CCannataro M(2022)An Italian lexicon-based sentiment analysis approach for medical applicationsProceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3535508.3545594(1-4)Online publication date: 7-Aug-2022
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Adukia AChrist CDas ARaj A(2022)Portrayals of Race and Gender: Sentiment in 100 Years of Children’s LiteratureProceedings of the 5th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies10.1145/3530190.3534811(20-28)Online publication date: 29-Jun-2022
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Sazzed SBellogín ABoratto LCena F(2022)Identifying neutral reviews from unlabeled data: An exploratory study on user ratings and word-level polarity scoresProceedings of the 33rd ACM Conference on Hypertext and Social Media10.1145/3511095.3536367(198-202)Online publication date: 28-Jun-2022
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