Feature-Based Summarizing and Ranking from Customer Reviews
Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1099982Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2524
 B. Liu, “Sentiment Analysis and Subjectivity. Handbook of Natural Language Processing”, Second Edition, (editors: N. Indurkhya and F. J. Damerau), 2010.
 A. Kamal, “Subjectivity Classification using Machine Learning Techniques for Mining Feature-Opinion Pairs from Web Opinion Sources”, New Delhi – 110025, India.
 J. Wiebe and E. Riloff, “Creating Subjective and Objective Sentence Classifiers from Unannotated Texts”.
 T. Ahmad and M.N Doja, “Rule Based System for Enhancing Recall for Feature Mining from Short Sentences in Customer Review Documents”, International Journal on Computer Science and Engineering (IJCSE), ISSN : 0975-3397, Vol. 4 No. 06, June 2012.
 S.S. Htay and K.T. Lynn, “Extracting Product Features and Opinion Words Using Pattern Knowledge in Customer Reviews”, The Scientific World Journal, Volume 2013 (2013), Article ID 394758, September 2013.
 G. Somprasertsri and P. Lalitrojwong, “Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization”, Journal of Universal Computer Science, vol. 16, no. 6 (2010), 938-955, March 2010.
 M. Hu and B. Liu, “Mining and Summarizing Customer Reviews”, KDD’04, Seattle, Washington, USA, August 22–25, 2004.
 F. Wogenstein, J. Drescher, D. Reinel, S. Rill and J. Scheidt, “Evaluation of an Algorithm for Aspect-Based Opinion Mining Using a Lexicon-Based Approach”, WISDOM ’13, Chicago, USA, August 2013.
 B.Liu, “Web Data Mining”, Springer, 2008.
 X. Ding, B. Liu, P.S. Yu, “A Holistic Lexicon-Based Approach to Opinion Mining”, WSDM’08, Palo Alto, California, USA, February, 2008.
 J.I. Sheeba and Dr.K. Vivekanandan, “Improved Sentiment Classification From Meeting Transcripts”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 5, September 2012.
 S. Rill, S. Adolph, J. Drescher, N. Korfiatis, D. Reinel, J. Scheidt, O. Sch¨ utz, F. Wogenstein, R. V. Zicari, “A Phrase-Based Opinion List for the German Language”.
 A. Esuli and F. Sebastiani. SentiWordNet: a publicly available lexical resource for opinion mining. In Proc. of LREC 2006 - 5th Conf. on Language Resources and Evaluation, Volume 6, 2006.
 F. Batista, R. Ribeiro, “Sentiment Analysis and Topic Classification based on Binary Maximum Entropy Classifiers”, Procesamiento del Lenguaje Natural, Revista nº 50 marzo de 2013, pp 77-84.
 K. Fragos, Y. Maistros and C. Skourlas, “A Weighted Maximum Entropy Language Model for Text Classification”.