Abstract
The last decade lead to an unbelievable growth of the importance of social media. Due to the huge amounts of documents appearing in social media, there is an enormous need for the automatic analysis of such documents. In this work, we focus on the analysis of documents appearing in blogs. We present a proof-of-concept industrial application, developed in cooperation with Capgemini Magyarország Kft. The most interesting component of this software prototype allows to predict the number of feedbacks that a blog document is expected to receive. For the prediction, we used various predictions algorithms in our experiments. For these experiments, we crawled blog documents from the internet. As an additional contribution, we published our dataset in order to motivate research in this field of growing interest.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
In order to quantify how discriminative is a word w, we use the average and standard deviation of the number of feedbacks of documents that contain w, and the average and standard deviation of the number of feedbacks of documents that do not contain w. Then, we divide the difference of the number of average feedbacks with the sum of the both standard deviations. Then, we selected the 200 most discriminative words.
References
Marinho, L. B., Buza, K., & Schmidt-Thieme, L. (2008). Folksonomy-based collabulary learning. The Semantic Web—ISWC 2008. Lecture Notes in Computer Science (Vol. 5318) (pp. 261–276). Heidelberg: Springer.
Mishne, G. (2007). Using blog properties to improve retrieval. In International Conference on Weblogs and Social Media (ICWSM’2007), Boulder, CO. http://www.icwsm.org/papers/3--Mishne.pdf.
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Journal Foundations and Trends in Information Retrieval, 2, 1–135.
Pinto, J. P. G. S. (2008). Detection methods for blog trends. Report of Dissertation Master in Informatics and Computing Engineering. Faculdade de Engenharia da Universidade do Porto.
Reuter, T., Cimiano, P., Drumond, L., Buza, K., & Schmidt-Thieme, L. (2011). Scalable event-based clustering of social media via record linkage techniques. In 5th International AAAI Conference on Weblogs and Social Media. Menlo Park: The AAAI Press.
Tan, P. N., Steinbach, M., & Kumar, V. (2006) Introduction to data mining. Boston: Pearson Addison Wesley.
Witten, I. H., & Franke, E. (2005). Data mining. Practical machine learning tools and techniques (2nd ed.). San Francisco: Elsevier.
Yano, T., & Smith, N. A. (2010). What’s worthy of comment? Content and comment volume in political blogs. In 4th International AAAI Conference on Weblogs and Social Media (pp. 359–362). Menlo Park: The AAAI Press.
Acknowledgements
We thank Capgemini Magyarország Kft. for the financial support of the project. The work reported in the paper has been developed in the framework of the project “Talent care and cultivation in the scientific workshops of BME” project. This project is supported by the grant TÁMOP-4.2.2.B-10/1–2010-0009.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Buza, K. (2014). Feedback Prediction for Blogs. In: Spiliopoulou, M., Schmidt-Thieme, L., Janning, R. (eds) Data Analysis, Machine Learning and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-01595-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-01595-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01594-1
Online ISBN: 978-3-319-01595-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)