A Novel Web Metric for the Evaluation of Internet Trends
Web 2.0 (social networking, blogging and online
forums) can serve as a data source for social science research because
it contains vast amount of information from many different users.
The volume of that information has been growing at a very high rate
and becoming a network of heterogeneous data; this makes things
difficult to find and is therefore not almost useful. We have proposed
a novel theoretical model for gathering and processing data from
Web 2.0, which would reflect semantic content of web pages in
better way. This article deals with the analysis part of the model and
its usage for content analysis of blogs. The introductory part of the
article describes methodology for the gathering and processing data
from blogs. The next part of the article is focused on the evaluation
and content analysis of blogs, which write about specific trend.
[1] M. Thelwall, P. Wouters, and J. Fry, "Information-centered research for
large-scale analyses of new information sources," Journal of the
American Society for Information Science and Technology, vol. 59, pp.
1523-1527, July 2008. [Online]. Available:
http://dx.doi.org/10.1002/asi.v59:9
[2] Internet World Stats - Usage and Population Statistics, "Internet usage
statistics." [Online]. Available: http://www.internetworldstats.com/stats.
htm
[3] S.-K. Han, D. Shin, J.-Y. Jung, and J. Park, "Exploring the relationship
between keywords and feed elements in blog post search," World Wide
Web, vol. 12, pp. 381-398, 2009, 10.1007/s11280-009-0067-3. [Online].
Available: http://dx.doi.org/10.1007/s11280-009-0067-3
[4] M. Thelwall, "Blog searching: The first general-purpose source of
retrospective public opinion in the social sciences?" 289, vol. 31, pp.
277+, 2007. [Online].Available:
http://dx.doi.org/10.1108/14684520710764069
[5] B. Pang and L. Lee, "Opinion mining and sentiment analysis,"
Foundations and Trends in Information Retrieval, vol. 2, no. 1-2, pp. 1-
135, Jan. 2008.
[6] R. MalinskÛ and I. Jelínek, "Improvements of webometrics by using
sentiment analysis for better accessibility of the web," in Current Trends
in Web Engineering, ser. Lecture Notes in Computer Science, F. Daniel
and F. Facca, Eds. Springer Berlin / Heidelberg, vol. 6385, pp. 581-586.
[Online]. Available: http://dx.doi.org/10.1007/978-3-642-16985-4 59
[7] I. Aguillo, J. Ortega, M. Fern'andez, and A. Utrilla, "Indicators for a
webometric ranking of open access repositories," Scientometrics, vol.
82, pp. 477-486, 2010, 10.1007/s11192-010-0183-y. [Online].
Available: http://dx.doi.org/10.1007/s11192-010-0183-y
[8] M. Thelwall, "Introduction to webometrics: Quantitative web research
for the social sciences." San Rafael, CA : Morgan & Claypool, 2009.
[9] M. Potthast and S. Becker, "Opinion summarization of web comments,"
in Advances in Information Retrieval, ser. Lecture Notes in Computer
Science, C. Gurrin, Y. He, G. Kazai, U. Kruschwitz, S. Little, T.
Roelleke, S. R├╝ger, and K. van Rijsbergen, Eds. Springer Berlin /
Heidelberg, 2010, vol. 5993, pp. 668-669, 10.1007/978-3-642-12275-0
73. [Online]. Available: http://dx.doi.org/10.1007/978-3-642-12275-0 73
[10] R. Prabowo and M. Thelwall, "Sentiment analysis: A combined
approach," Journal of Informetrics, vol. 3, no. 2, pp. 143-157, 2009.
[11] K. Toutanova, D. Klein, C. Manning, and Y. Singer, "Feature-rich partof-
speech tagging with a cyclic dependency network," in HLT-NAACL,
2003, pp. 252-259.
[12] R. Agrawal, S. Gollapudi, K. Kenthapadi, N. Srivastava, and R. Velu,
"Enriching textbooks through data mining," in Proceedings of the First
ACM Symposium on Computing for Development, ser. ACM DEV -10.
New York, NY, USA: ACM, 2010, pp. 19:1-19:9. [Online]. Available:
http://doi.acm.org/10.1145/1926180.1926204
[13] S. Baccianella, A. Esuli, and F. Sebastiani, "Sentiwordnet 3.0: An
enhanced lexical resource for sentiment analysis and opinion mining," in
Proceedings of the Seventh conference on International Language
Resources and Evaluation (LREC-10), N. Calzolari, K. Choukri, B.
Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner, and D. Tapias,
Eds. Valletta, Malta: European Language Resources Association
(ELRA), may 2010.
[14] A. Esuli and F. Sebastiani, "Sentiwordnet: A publicly available lexical
resource for opinion mining," in In Proceedings of the 5th Conference
on Language Resources and Evaluation (LREC-06), 2006, pp. 417-422.
[1] M. Thelwall, P. Wouters, and J. Fry, "Information-centered research for
large-scale analyses of new information sources," Journal of the
American Society for Information Science and Technology, vol. 59, pp.
1523-1527, July 2008. [Online]. Available:
http://dx.doi.org/10.1002/asi.v59:9
[2] Internet World Stats - Usage and Population Statistics, "Internet usage
statistics." [Online]. Available: http://www.internetworldstats.com/stats.
htm
[3] S.-K. Han, D. Shin, J.-Y. Jung, and J. Park, "Exploring the relationship
between keywords and feed elements in blog post search," World Wide
Web, vol. 12, pp. 381-398, 2009, 10.1007/s11280-009-0067-3. [Online].
Available: http://dx.doi.org/10.1007/s11280-009-0067-3
[4] M. Thelwall, "Blog searching: The first general-purpose source of
retrospective public opinion in the social sciences?" 289, vol. 31, pp.
277+, 2007. [Online].Available:
http://dx.doi.org/10.1108/14684520710764069
[5] B. Pang and L. Lee, "Opinion mining and sentiment analysis,"
Foundations and Trends in Information Retrieval, vol. 2, no. 1-2, pp. 1-
135, Jan. 2008.
[6] R. MalinskÛ and I. Jelínek, "Improvements of webometrics by using
sentiment analysis for better accessibility of the web," in Current Trends
in Web Engineering, ser. Lecture Notes in Computer Science, F. Daniel
and F. Facca, Eds. Springer Berlin / Heidelberg, vol. 6385, pp. 581-586.
[Online]. Available: http://dx.doi.org/10.1007/978-3-642-16985-4 59
[7] I. Aguillo, J. Ortega, M. Fern'andez, and A. Utrilla, "Indicators for a
webometric ranking of open access repositories," Scientometrics, vol.
82, pp. 477-486, 2010, 10.1007/s11192-010-0183-y. [Online].
Available: http://dx.doi.org/10.1007/s11192-010-0183-y
[8] M. Thelwall, "Introduction to webometrics: Quantitative web research
for the social sciences." San Rafael, CA : Morgan & Claypool, 2009.
[9] M. Potthast and S. Becker, "Opinion summarization of web comments,"
in Advances in Information Retrieval, ser. Lecture Notes in Computer
Science, C. Gurrin, Y. He, G. Kazai, U. Kruschwitz, S. Little, T.
Roelleke, S. R├╝ger, and K. van Rijsbergen, Eds. Springer Berlin /
Heidelberg, 2010, vol. 5993, pp. 668-669, 10.1007/978-3-642-12275-0
73. [Online]. Available: http://dx.doi.org/10.1007/978-3-642-12275-0 73
[10] R. Prabowo and M. Thelwall, "Sentiment analysis: A combined
approach," Journal of Informetrics, vol. 3, no. 2, pp. 143-157, 2009.
[11] K. Toutanova, D. Klein, C. Manning, and Y. Singer, "Feature-rich partof-
speech tagging with a cyclic dependency network," in HLT-NAACL,
2003, pp. 252-259.
[12] R. Agrawal, S. Gollapudi, K. Kenthapadi, N. Srivastava, and R. Velu,
"Enriching textbooks through data mining," in Proceedings of the First
ACM Symposium on Computing for Development, ser. ACM DEV -10.
New York, NY, USA: ACM, 2010, pp. 19:1-19:9. [Online]. Available:
http://doi.acm.org/10.1145/1926180.1926204
[13] S. Baccianella, A. Esuli, and F. Sebastiani, "Sentiwordnet 3.0: An
enhanced lexical resource for sentiment analysis and opinion mining," in
Proceedings of the Seventh conference on International Language
Resources and Evaluation (LREC-10), N. Calzolari, K. Choukri, B.
Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner, and D. Tapias,
Eds. Valletta, Malta: European Language Resources Association
(ELRA), may 2010.
[14] A. Esuli and F. Sebastiani, "Sentiwordnet: A publicly available lexical
resource for opinion mining," in In Proceedings of the 5th Conference
on Language Resources and Evaluation (LREC-06), 2006, pp. 417-422.
@article{"International Journal of Information, Control and Computer Sciences:53633", author = "Radek Malinský and Ivan Jelínek", title = "A Novel Web Metric for the Evaluation of Internet Trends", abstract = "Web 2.0 (social networking, blogging and online
forums) can serve as a data source for social science research because
it contains vast amount of information from many different users.
The volume of that information has been growing at a very high rate
and becoming a network of heterogeneous data; this makes things
difficult to find and is therefore not almost useful. We have proposed
a novel theoretical model for gathering and processing data from
Web 2.0, which would reflect semantic content of web pages in
better way. This article deals with the analysis part of the model and
its usage for content analysis of blogs. The introductory part of the
article describes methodology for the gathering and processing data
from blogs. The next part of the article is focused on the evaluation
and content analysis of blogs, which write about specific trend.", keywords = "Blog, Sentiment Analysis, Web 2.0, Webometrics", volume = "5", number = "9", pages = "985-4", }
