Abstract
The exponential increase of subjective, user-generated content since the birth of the Social Web, has led to the necessity of developing automatic text processing systems able to extract, process and present relevant knowledge. In this paper, we tackle the Opinion Retrieval, Mining and Summarization task, by proposing a unified framework, composed of three crucial components (information retrieval, opinion mining and text summarization) that allow the retrieval, classification and summarization of subjective information. An extensive analysis is conducted, where different configurations of the framework are suggested and analyzed, in order to determine which is the best one, and under which conditions. The evaluation carried out and the results obtained show the appropriateness of the individual components, as well as the framework as a whole. By achieving an improvement over 10% compared to the state-of-the-art approaches in the context of blogs, we can conclude that subjective text can be efficiently dealt with by means of our proposed framework.
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Document Understanding Conference http://duc.nist.gov (Last Access: 06/02/2012).
http://www.ciao.co.uk/ (Last Access: 06/02/2012).
http://www.swotti.com (Last Access: 06/02/2012).
http://www.nist.gov/tac/2008/summarization/op.summ.08.guidelines.html (Last Access: 06/02/2012).
http://alias-i.com/lingpipe/ (Last Access: 06/02/2012).
http://www.yahoo.com/ (Last Access: 06/02/2012).
http://infomap-nlp.sourceforge.net/ (Last Access: 06/02/2012).
http://duc.nist.gov/duc2004/software/duc2003.breakSent.tar.gz (Last Access: 06/02/2012).
http://cogcomp.cs.illinois.edu/page/tools_view/8 (Last Access: 06/02/2012).
http://tartarus.org/~martin/PorterStemmer/ (Last Access: 06/02/2012).
http://www.ims.uni-stuttgart.de/projekte/corplex/TreeTagger/ (Last Access: 06/02/2012).
http://jmlr.csail.mit.edu/papers/volume5/lewis04a/a11-smart-stop-list/english.stop (Last Access: 06/02/2012).
http://www.nist.gov/tac/data/past/2008/OpSummQA08.html (Last Access: 06/02/2012).
http://www.nist.gov/tac/data/past/2008/OpSummQA08.html (Last Access: 06/02/2012).
For specific detail of the different IR, OM and TS components, please refer to Section 3.
http://www.d.umn.edu/~tpederse/text-similarity.html (Last Access: 06/02/2012).
The cosine similarity was computed using Pedersen’s Text Similarity Package: http://www.d.umn.edu/~tpederse/text-similarity.html (Last Access: 06/02/2012).
We can consider opinion question answering as a specific type of information retrieval.
A t-test was carried out in order to account for the significance of the results.
We have used these snippets for building our QA-snippets baseline.
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Acknowledgements
We would like to thank all the anonymous reviewers for their useful comments and suggestions. This research work has been funded by the Spanish Government through the project TEXT-MESS 2.0 (TIN2009-13391-C04) and by the Valencian Government through projects PROMETEO (PROMETEO/2009/199) and ACOMP/2011/001.
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Lloret, E., Balahur, A., Gómez, J.M. et al. Towards a unified framework for opinion retrieval, mining and summarization. J Intell Inf Syst 39, 711–747 (2012). https://doi.org/10.1007/s10844-012-0209-4
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DOI: https://doi.org/10.1007/s10844-012-0209-4