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Feature-Opinion Extraction using Formal and Informal Classifier Technique

by Jennifer Selvaraj, J. W. Bakal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 95 - Number 18
Year of Publication: 2014
Authors: Jennifer Selvaraj, J. W. Bakal
10.5120/16697-6824

Jennifer Selvaraj, J. W. Bakal . Feature-Opinion Extraction using Formal and Informal Classifier Technique. International Journal of Computer Applications. 95, 18 ( June 2014), 33-37. DOI=10.5120/16697-6824

@article{ 10.5120/16697-6824,
author = { Jennifer Selvaraj, J. W. Bakal },
title = { Feature-Opinion Extraction using Formal and Informal Classifier Technique },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 95 },
number = { 18 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 33-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume95/number18/16697-6824/ },
doi = { 10.5120/16697-6824 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:19:48.587227+05:30
%A Jennifer Selvaraj
%A J. W. Bakal
%T Feature-Opinion Extraction using Formal and Informal Classifier Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 95
%N 18
%P 33-37
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Merchants selling products on the Web often ask their customers to share their opinions and hands-on experiences on products they have purchased. This is not only true for organizations but also true for individuals. Our beliefs and perceptions of reality, and the choices we make, are, to a considerable degree, conditioned upon how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. Unfortunately, reading through all customer reviews is difficult, especially for popular items, the number of reviews can be up to hundreds or even thousands. This makes it difficult for a potential customer to read them and make an informed decision. Thus a compressed and precise opinion list is what a user would generally desire. The contents available on the Web are not in the standard format. The idea is to classify these contents as formal and informal. The type of algorithm used is linguistic. It incorporates grammatical and other knowledge of the language which helps in understanding the text, thus trying to improve the mining approach to mine product features and their opinions from Web opinion sources for formal as well as for informal text.

References
  1. Fadi Abu Sheikha and Diana Inkpen, "Automatic Classification of documents by formality", IEEE 2010.
  2. Francis Heylinghen and Jean-Marc Dewaele, "Formality of language: definition and measurement", Internal Report, Center "Leo Apostel", Free University of Brussels, 1999.
  3. K. B. Dempsey, P. M. McCarthy, and D. S. McNamara, "Using phrasal verbs as an index to distinguish text genres", In D. Wilson and G. Sutcliffe (Eds. ), Proceedings of the twentieth International Florida Artificial Intelligence Research Society Conference (pp. 217-222). Menlo Park, California: The AAAI Press, Feb. 2007.
  4. A. Kennedy and M. Shepherd, "Automatic Identification of Home Pages on the Web", Proceedings of the 38th Hawaii International Conference on System Sciences, 2005.
  5. Yu-shan Chang and Yun-Hsuan Sung, "Applying Name Entity Recognition to Informal Text", Ling 237 Final Projects, 2005.
  6. P. Tapanainen and Jarvinen Timo, "A nonprojective dependency parser", In Proceedings of the 5th Conference on Applied Natural Language Processing, pages 64-71, Washington D. C. Association for Computational Linguistics, 1997.
  7. Sowmya Kamath S, Anusha Bagalkotkar, Ashesh Khandelwal, Shivam Pandey, Kumari Poornima, "Sentiment Analysis Based Approaches for Understanding User Context in Web Content", IEEE International Conference on Communication Systems and Network Technologies, pp. 607-611, 2013.
  8. Luole Qi and Li Chen, "Comparison of Model-Based Learning Methods for Feature-Level Opinion Mining", IEEE International Conferences on Web Intelligence and Intelligent Agent Technology, pp. 265-273, 2011.
  9. Yin-Fu Huang and Heng Lin, "Web Product Ranking Using Opinion Mining", IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 184-190, 2013.
  10. Weishu Hu, Zhiguo Gong and Jingzhi Guo, "Mining Product Features from Online Reviews", IEEE International Conference on E-Business Engineering, pp. 24-29, 2010.
  11. Lizhen Liu, Zhixin Lv and Hanshi Wang, "Opinion Mining Based on Feature-Level", IEEE International Congress on Image and Signal Processing (CISP), pp. 1596-1600, 2012.
  12. V. K. Singh, R. Piryani, A. Uddin, P. Waila, "Sentiment Analysis of Movie Reviews - A new Feature-based Heuristic for Aspect-level Sentiment Classification", IEEE, pp. 712-717, 2013.
  13. Liu Gongshen, Lai Huoyao and Luo Jun, Lin Jiuchuan, "Predicting the Semantic Orientation of Movie Reviews", IEEE International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2010), pp. 2483-2487, 2010.
  14. Ahmad Kamal, Muhammad Abulaish, Tarique Anwar, "Mining Feature-Opinion Pairs and Their Reliability Scores from Web Opinion Sources". WIMS? 12, June 13-15, Craiova, Romania, 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Online informal formal tagging parse

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