article

Blog mining-review and extensions: "From each according to his opinion"

Author:

Daniel E. O'LearyAuthors Info & Claims

Decision Support Systems, Volume 51, Issue 4

Pages 821 - 830

https://doi.org/10.1016/j.dss.2011.01.016

Published: 01 November 2011 Publication History

Abstract

Blogs provide a type of website that contains information and personal opinions of the individual authors. The purpose of this paper is to review some of the literature aimed at gathering opinion, sentiment and information from blogs. This paper also extends the previous literature in a number of directions, extending the use of knowledge from tags on blogs, finding the need for domain specific terms to capture a richer understanding of mood of a blog and finding a relationship between information in message boards and blogs. The relationship between blog chatter and sales, and blogs and public image are also examined.

References

[1]

Antweiler, W. and Frank, M.Z., Is all of that talk, just noise? The information content of Internet stock message boards. Journal of Finance. v59 i3. 1259-1295.

[2]

http://arxiv.org/PS_cache/arxiv/pdf/1003/1003.5699v1.pdf

[3]

http://trec.nist.gov/pubs/trec15/t15_proceedings.html

[4]

Balog, K. and Rijke, M., Decomposing Bloggers' Moods, WWW2006, Edinburgh, UK. May 22-26 2006.

[5]

http://www.theregister.co.uk/2005/11/09/ibm_blog_brand_software/

[6]

http://www.pcworld.idg.com.au/article/205589/blog_chatter_can_triple_music_sales/?fp=&fpid=&pf=1

[7]

Dhar, V. and Chang, E., Does chatter matter?. In: The Impact of User Generated Content on Music Sales, NYU Working Paper,

[8]

Glionna, J., . In: Workers Warned of Dangers at Toyota, Los Angeles Times, Monday,

[9]

Gruhl, D., Guha, R., Kumar, R., Novak, J. and Tomlins, A., The Predictive Power of Online Chatter, in KDD 05 Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 78-87. 2005. ACM Press, New York.

[10]

http://www.internetnews.com/xSP/article.php/3562116

[11]

http://www.ibm.com/developerworks/data/library/techarticle/dm-0508lang/

[12]

Hayek, F., The use of knowledge in society. The American Economic Review. v35. 519-530.

[13]

Combining Local and Global Resources for Constructing an Error-Minimized Opinion Word Dictionary, PRICAI 2008, LNAI 5351, 688-697. 2008. Springer-Verlag, Berlin.

[14]

http://www-03.ibm.com/press/us/en/pressrelease/7961.wss

[15]

Kim, S. and Hovy, E., Determining the Sentiment of Opinions. In: Proceedings of the COLING, Geneva,

[16]

http://www.dailyfinance.com/story/investing/goldman-shares-tumble-as-public-image-takes-another-hit/19460940/

[17]

L A Times, . In: Magistrates Walk Out Over Changes, Los Angeles Times, pp. A26

[18]

http://news.cnet.com/IBM-to-analyze-digital-scuttlebutt/2100-1012_3-5940339.html

[19]

Lavrenko, V., Schmill, M., Lawrie, D., Ogilvie, P., Jensen, D. and Allan, J., Language Models for Financial News Recommendations. In: Proceedings of the Ninth International Conference on Information and Knowledge Management, ACM Press, New York, NY. pp. 389-396.

Digital Library

[20]

http://www.commoncraft.com/what-are-differences-between-message-boards-and-weblogs-15

[21]

A. Lerman, Individual Investor's Attention to Accounting Information: Message Board Discussions, unpublished paper, New York University (Jan. 2010).

[22]

Liu, K. and Zhao, J., NLPR at Multilingual Opinion Task in NTCIR7. In: Proceedings of the NTCIR-7 Workshop Meeting, Tokyo, Japan,

[23]

Liu, Y., Huang, X., Ari, A. and Yu, X., . In: ARSA: A Sentiment Aware Model for Predicting Sales Performance Using Blogs, SIGIR '07, The Netherlands,

[24]

Mann, C., Spam+Blogs=Trouble. Wired. v14 i9.

[25]

Mishne, G. and Glance, N., Predicting Movie Sales from Blogger Sentiment. 2005. American Association for Artificial Intelligence.

[26]

Mishne, G. and Rijke, M., Moodviews: Tools for Blog Mood Analysis. 2006. American Association for Artificial Intelligence.

[27]

Nonaka, I., The knowledge creating company. Harvard Business Review. v69. 96-104.

[28]

Polanyi, M., The Tacit Dimension. 1966. Routledge & Kegan Paul, London, UK.

[29]

Ramstad, E., . In: Critic of Seoul Arrested, The Wall Street Journal. pp. 3

[30]

Sabherwal, S., Sarkar, S. and Zhang, Y., Online Talk: Does it Matter. Managerial Finance. v34 i6. 423-436.

[31]

Tong, R., . In: Detecting and Tracking Opinions in On-Line Discussions, UCB/SIMS Web Mining Workshop,

[32]

Toulmin, S., The Uses of Argument. 1964. Cambridge University Press, Cambrdige England.

[33]

Tumarkin, R., Internet Message Board Activity and Market Efficiency: A Case Study of the Internet Service Sector Using RagingBull.com. Financial Markets, Institutions and Instruments. v11 i4. 313-335.

[34]

Vara, V., Companies Mine Blogs for Market Research. Dec. 3 2004. Wall Street Journal.

[35]

Xinfan, M. and Houfeng, W., Detecting opinionated sentences by extracting context information. In: Proceedings of the NTCIR-7 Workshop Meeting, Tokyo, Japan,

[36]

http://trec.nist.gov/pubs/trec15/t15_proceedings.html

Cited By

Singh RSachan MPatel R(2021)360 degree view of cross-domain opinion classification: a surveyArtificial Intelligence Review10.1007/s10462-020-09884-954:2(1385-1506)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s10462-020-09884-9
Chen L(2019)Based on The Document-Link and Time-Clue Relationships Between Blog Posts to Improve the Performance of Google Blog SearchInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.201901010315:1(52-75)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.4018/IJSWIS.2019010103
Yousif ANiu ZTarus JAhmad A(2019)A survey on sentiment analysis of scientific citationsArtificial Intelligence Review10.1007/s10462-017-9597-852:3(1805-1838)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s10462-017-9597-8
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Published In

cover image Decision Support Systems

Decision Support Systems Volume 51, Issue 4

November, 2011

206 pages

ISSN:0167-9236

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2011.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 November 2011

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Cited By

Singh RSachan MPatel R(2021)360 degree view of cross-domain opinion classification: a surveyArtificial Intelligence Review10.1007/s10462-020-09884-954:2(1385-1506)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s10462-020-09884-9
Chen L(2019)Based on The Document-Link and Time-Clue Relationships Between Blog Posts to Improve the Performance of Google Blog SearchInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.201901010315:1(52-75)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.4018/IJSWIS.2019010103
Yousif ANiu ZTarus JAhmad A(2019)A survey on sentiment analysis of scientific citationsArtificial Intelligence Review10.1007/s10462-017-9597-852:3(1805-1838)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s10462-017-9597-8
Deng SHuang ZSinha AZhao H(2018)The interaction between microblog sentiment and stock returnsMIS Quarterly10.25300/MISQ/2018/1426842:3(895-918)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.25300/MISQ/2018/14268
Deng SSinha AZhao H(2017)Adapting sentiment lexicons to domain-specific social media textsDecision Support Systems10.1016/j.dss.2016.11.00194:C(65-76)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1016/j.dss.2016.11.001
Wang BHuang YYuan ZLi X(2016)A multi-granularity fuzzy computing model for sentiment classification of Chinese reviewsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/IFS-15185330:3(1445-1460)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.3233/IFS-151853
O'Leary D(2016)On the relationship between number of votes and sentiment in crowdsourcing ideas and comments for innovationDecision Support Systems10.1016/j.dss.2016.05.00688:C(28-37)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1016/j.dss.2016.05.006
Oliveira NCortez PAreal N(2016)Stock market sentiment lexicon acquisition using microblogging data and statistical measuresDecision Support Systems10.1016/j.dss.2016.02.01385:C(62-73)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1016/j.dss.2016.02.013
Goel SUzuner O(2016)Do Sentiments Matter in Fraud Detection? Estimating Semantic Orientation of Annual ReportsInternational Journal of Intelligent Systems in Accounting and Finance Management10.1002/isaf.139223:3(215-239)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1002/isaf.1392
Fisher IGarnsey MHughes M(2016)Natural Language Processing in Accounting, Auditing and FinanceInternational Journal of Intelligent Systems in Accounting and Finance Management10.1002/isaf.138623:3(157-214)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1002/isaf.1386
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