research-article

Free access

Enhanced sentiment learning using Twitter hashtags and smileys

Authors:

Dmitry Davidov,

Ari RappoportAuthors Info & Claims

COLING '10: Proceedings of the 23rd International Conference on Computational Linguistics: Posters

Pages 241 - 249

Published: 23 August 2010 Publication History

Abstract

Automated identification of diverse sentiment types can be beneficial for many NLP systems such as review summarization and public media analysis. In some of these systems there is an option of assigning a sentiment value to a single sentence or a very short text.

In this paper we propose a supervised sentiment classification framework which is based on data from Twitter, a popular microblogging service. By utilizing 50 Twitter tags and 15 smileys as sentiment labels, this framework avoids the need for labor intensive manual annotation, allowing identification and classification of diverse sentiment types of short texts. We evaluate the contribution of different feature types for sentiment classification and show that our framework successfully identifies sentiment types of untagged sentences. The quality of the sentiment identification was also confirmed by human judges. We also explore dependencies and overlap between different sentiment types represented by smileys and Twitter hashtags.

References

[1]

Akkaya, Cem, Janyce Wiebe, and Rada Mihalcea. 2009. Subjectivity word sense disambiguation. In EMNLP.

Digital Library

[2]

Andreevskaia, A. and S. Bergler. 2006. Mining wordnet for fuzzy sentiment: Sentiment tag extraction from wordnet glosses. In EACL.

[3]

Balog, Krisztian, Gilad Mishne, and Maarten de Rijke. 2006. Why are they excited? identifying and explaining spikes in blog mood levels. In EACL.

Digital Library

[4]

Bloom, Kenneth, Navendu Garg, and Shlomo Argamon. 2007. Extracting appraisal expressions. In HLT/NAACL.

[5]

Davidov, D. and A. Rappoport. 2006. Efficient unsupervised discovery of word categories using symmetric patterns and high frequency words. In COLING-ACL.

Digital Library

[6]

Davidov, D. and A. Rappoport. 2008. Unsupervised discovery of generic relationships using pattern clusters and its evaluation by automatically generated sat analogy questions. In ACL.

[7]

Davidov, D., O. Tsur, and A. Rappoport. 2010. Semi-supervised recognition of sarcastic sentences in twitter and amazon. In CoNLL.

Digital Library

[8]

Esuli, Andrea and Fabrizio Sebastiani. 2006. Senti-wordnet: A publicly available lexical resource for opinion mining. In LREC.

[9]

Jansen, B. J., M. Zhang, K. Sobel, and A. Chowdury. 2009. Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology.

Digital Library

[10]

Kim, S. M. and E. Hovy. 2004. Determining the sentiment of opinions. In COLING.

Digital Library

[11]

McDonald, Ryan, Kerry Hannan, Tyler Neylon, Mike Wells, and Jeff Reynar. 2007. Structured models for fine-to-coarse sentiment analysis. In ACL.

[12]

Melville, Prem, Wojciech Gryc, and Richard D. Lawrence. 2009. Sentiment analysis of blogs by combining lexical knowledge with text classification. In KDD. ACM.

Digital Library

[13]

Mihalcea, Rada and Hugo Liu. 2006. A corpus-based approach to finding happiness. In In AAAI 2006 Symposium on Computational Approaches to Analysing Weblogs. AAAI Press.

[14]

Mishne, Gilad. 2005. Experiments with mood classification in blog posts. In Proceedings of the 1st Workshop on Stylistic Analysis Of Text.

[15]

Riloff, Ellen. 2003. Learning extraction patterns for subjective expressions. In EMNLP.

Digital Library

[16]

Strapparava, Carlo and Rada Mihalcea. 2008. Learning to identify emotions in text. In SAC.

Digital Library

[17]

Titov, Ivan and Ryan McDonald. 2008a. A joint model of text and aspect ratings for sentiment summarization. In ACL/HLT, June.

[18]

Titov, Ivan and Ryan McDonald. 2008b. Modeling online reviews with multi-grain topic models. In WWW, pages 111--120, New York, NY, USA. ACM.

Digital Library

[19]

Tsur, Oren, Dmitry Davidov, and Ari Rappoport. 2010. Icwsm - a great catchy name: Semi-supervised recognition of sarcastic sentences in product reviews. In AAAI-ICWSM.

[20]

Turney, Peter D. 2002. Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In ACL '02, volume 40.

Digital Library

[21]

Whitelaw, Casey, Navendu Garg, and Shlomo Argamon. 2005. Using appraisal groups for sentiment analysis. In CIKM.

Digital Library

[22]

Wiebe, Janyce and Rada Mihalcea. 2006. Word sense and subjectivity. In COLING/ACL, Sydney, AUS.

Digital Library

[23]

Wiebe, Janyce M. 2000. Learning subjective adjectives from corpora. In AAAI.

Digital Library

[24]

Wilson, Theresa, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In HLT/EMNLP.

Digital Library

[25]

Wilson, Theresa, Janyce Wiebe, and Paul Hoffmann. 2009. Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational Linguistics, 35(3):399--433.

Digital Library

[26]

Yu, Hong and Vasileios Hatzivassiloglou. 2003. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In EMNLP.

Digital Library

Cited By

Wang QXu HWen ZLiang BYang MQin BXu R(2024)Image-to-Text Conversion and Aspect-Oriented Filtration for Multimodal Aspect-Based Sentiment AnalysisIEEE Transactions on Affective Computing10.1109/TAFFC.2023.333320015:3(1264-1278)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TAFFC.2023.3333200
Chen ZCao YYao HLu XPeng XMei HLiu X(2021)Emoji-powered Sentiment and Emotion Detection from Software Developers’ Communication DataACM Transactions on Software Engineering and Methodology10.1145/342430830:2(1-48)Online publication date: 27-Jan-2021
https://dl.acm.org/doi/10.1145/3424308
Al Bashaireh RZohdy MSabeeh V(2020)Twitter Data Collection and ExtractionProceedings of the 2020 the 4th International Conference on Information System and Data Mining10.1145/3404663.3404686(71-76)Online publication date: 15-May-2020
https://dl.acm.org/doi/10.1145/3404663.3404686
Show More Cited By

Index Terms

Enhanced sentiment learning using Twitter hashtags and smileys

Recommendations

Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach
CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

Twitter is one of the biggest platforms where massive instant messages (i.e. tweets) are published every day. Users tend to express their real feelings freely in Twitter, which makes it an ideal source for capturing the opinions towards various ...
Sentiment analysis of greek tweets and hashtags using a sentiment lexicon
PCI '15: Proceedings of the 19th Panhellenic Conference on Informatics

The rapid growth of social media has rendered opinion and sentiment mining an important area of research with a wide range of applications. We focus on the Greek language and the microblogging platform "Twitter", investigating methods for extracting ...
Aspect-Based Twitter Sentiment Classification
ICTAI '13: Proceedings of the 2013 IEEE 25th International Conference on Tools with Artificial Intelligence

Due to the popularity of Twitter, sentiment classification for Twitter has become a hot research topic. Previous studies have approached the problem as a tweet-level classification task where each tweet is classified as positive, negative or neutral. ...

Comments

Information & Contributors

Information

Published In

cover image DL Hosted proceedings

COLING '10: Proceedings of the 23rd International Conference on Computational Linguistics: Posters

August 2010

1588 pages

General Chair:
Aravind K. Joshi
University of Pennsylvania
,
Program Chairs:
Chu-Ren Huang
The Hong Kong Polytechnic University
,
Dan Jurafsky
Stanford University

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 23 August 2010

Qualifiers

Research-article

Acceptance Rates

Overall Acceptance Rate 1,537 of 1,537 submissions, 100%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

93
Total Citations
View Citations
4,765
Total Downloads

Downloads (Last 12 months)48
Downloads (Last 6 weeks)11

Reflects downloads up to 20 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wang QXu HWen ZLiang BYang MQin BXu R(2024)Image-to-Text Conversion and Aspect-Oriented Filtration for Multimodal Aspect-Based Sentiment AnalysisIEEE Transactions on Affective Computing10.1109/TAFFC.2023.333320015:3(1264-1278)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TAFFC.2023.3333200
Chen ZCao YYao HLu XPeng XMei HLiu X(2021)Emoji-powered Sentiment and Emotion Detection from Software Developers’ Communication DataACM Transactions on Software Engineering and Methodology10.1145/342430830:2(1-48)Online publication date: 27-Jan-2021
https://dl.acm.org/doi/10.1145/3424308
Al Bashaireh RZohdy MSabeeh V(2020)Twitter Data Collection and ExtractionProceedings of the 2020 the 4th International Conference on Information System and Data Mining10.1145/3404663.3404686(71-76)Online publication date: 15-May-2020
https://dl.acm.org/doi/10.1145/3404663.3404686
Javari AHe ZHuang ZJeetu RChen-Chuan Chang K(2020)Weakly Supervised Attention for Hashtag Recommendation using Graph DataProceedings of The Web Conference 202010.1145/3366423.3380182(1038-1048)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380182
Beseiso MElmousalami H(2020)Subword Attentive Model for Arabic Sentiment AnalysisACM Transactions on Asian and Low-Resource Language Information Processing10.1145/336001619:2(1-17)Online publication date: 13-Feb-2020
https://dl.acm.org/doi/10.1145/3360016
Botchway RJibril AKwarteng MChovancova MOplatková ZCiccarone GKumar V(2019)A review of social media posts from UniCredit bank in EuropeProceedings of the 3rd International Conference on Business and Information Management10.1145/3361785.3361814(74-79)Online publication date: 12-Sep-2019
https://dl.acm.org/doi/10.1145/3361785.3361814
Mittal APatidar S(2019)Sentiment Analysis on Twitter DataProceedings of the 7th International Conference on Computer and Communications Management10.1145/3348445.3348466(91-95)Online publication date: 27-Jul-2019
https://dl.acm.org/doi/10.1145/3348445.3348466
Chen ZCao YLu XMei QLiu XDumas MPfahl DApel SRusso A(2019)SEntiMoji: an emoji-powered learning approach for sentiment analysis in software engineeringProceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3338906.3338977(841-852)Online publication date: 12-Aug-2019
https://dl.acm.org/doi/10.1145/3338906.3338977
Chen ZShen SHu ZLu XMei QLiu X(2019)Emoji-Powered Representation Learning for Cross-Lingual Sentiment ClassificationThe World Wide Web Conference10.1145/3308558.3313600(251-262)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313600
Zhang Y(2019)Language in Our Time: An Empirical Analysis of HashtagsThe World Wide Web Conference10.1145/3308558.3313480(2378-2389)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313480
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents