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Auto Text Summarization with Categorization and Sentiment Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 130 - Number 7 |
| Year of Publication: 2015 |
| Authors: Ashmita Shetty, Ruhi Bajaj |
10.5120/ijca2015907065
|
Ashmita Shetty, Ruhi Bajaj . Auto Text Summarization with Categorization and Sentiment Analysis. International Journal of Computer Applications. 130, 7 ( November 2015), 57-60. DOI=10.5120/ijca2015907065
Abstract
In today’s world the volume of information is dramatically increasing, and the value of that information is growing fast. Modern organizations deals with terabytes of text, such as email, that often plays a significant role in their day-to-day operations. Even small and medium-sized organizations are dealing with growing volumes of text that require rapid access and meaningful analysis. Identifying useful information from these data is quite difficult and requires some mechanism. One possible means is to use text categorization and summarization. Text categorization is automatically arranging a set of documents into predefined categories and Summarization is a giving a condensed and precise depiction of input data such that the output includes the most significant concepts of the source. Sentiment analysis i.e. opinion mining states the use of NLP, text analysis and to identify and extract biased information in source materials.
References
- G. Padmapriya, Dr. K. Duraiswamy, “Association of Deep Learning Algorithm with Fuzzy Logic for Multidocument Text Summarization”, Journal of Theoretical and Applied Information Technology, April 2014.
- Seyed-Ali Bahrainian, Andreas Dengel, “Sentiment Analysis and Summarization of Twitter Data”, 2013 IEEE 16th International Conference on Computational Science and Engineering, 2013.
- JIANG Xiao-Yu, FAN Xiao-Zhong, Wang Zhi-Fei,JiaKe-Liang, “Improving the Performance of Text Categorization using Automatic Summarization”, International Conference on Computer Modeling and Simulation, 2009 IEEE.
- V. Gupta, G. S. Lehal, “A Survey of Text Mining Techniques and Applications”, Journal of Emerging Technologies in Web Intelligence, Vol. 1, No. 1, August 2009.
- C. Ramasubramanian, R. Ramya, “Effective Pre-Processing Activities in Text Mining using Improved Porter’s Stemming Algorithm”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, Issue 12, December 2013.
- L. Suanmali, N. Salim, M.S. Binwahlan, “SRL-GSM: A Hybrid Approach based on Semantic Role Labeling and General Statistic Method for Text Summarization”, Research Article- Journal of Applied Science, 2010.
- M. K. Dalal, M. A. Zaveri, “Semisupervised Learning Based Opinion Summarization and Classification for Online Product Reviews”, Hindawi Publishing Corporation Applied Computational Intelligence and Soft Computing, Volume 2013.
- A. Kiani, M. R. Akbarzadeh, “Automatic Text Summarization Using: Hybrid Fuzzy GA-GP”, International Conference on Fuzzy Systems, 2006 IEEE.
- R. D. Shinde, S. H. Routela, S. S. Jadhav, S. R. Sagare, “Enforcing Text Summarization using Fuzzy Logic”, International Journal of Computer Science and Information Techologies, Volume 5(6), 2014 IJCSIT.
- L. Suanmali, N. Salim, Mohammed Salem Binwahlan, “Feature-Based Sentence Extraction Using Fuzzy Inference rules”, International Conference on Signal Processing Systems, 2009 IEEE.
- S. Rana, “Sentiment Analysis for Hindi Text using Fuzzy Logic”, Indian Journal of Applied Research, Volume 4, Issue 8, August 2014.
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