Research Article | OPEN ACCESS
A Survey on Web Text Information Retrieval in Text Mining
1Tapaswini Nayak, 2Srinivash Prasad and 3Manas Ranjan Senapati
1Department of Computer Science, Centurion University, India
2GMR Institute of Technology, India
3Centurion University, India
Research Journal of Applied Sciences, Engineering and Technology 2015 10:1164-1174
Received: February 16, 2015 | Accepted: March 12, 2015 | Published: August 05, 2015
Abstract
In this study we have analyzed different techniques for information retrieval in text mining. The aim of the study is to identify web text information retrieval. Text mining almost alike to analytics, which is a process of deriving high quality information from text. High quality information is typically derived in the course of the devising of patterns and trends through means such as statistical pattern learning. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, creation of coarse taxonomies, sentiment analysis, document summarization and entity relation modeling. It is used to mine hidden information from not-structured or semi-structured data. This feature is necessary because a large amount of the Web information is semi-structured due to the nested structure of HTML code, is linked and is redundant. Web content categorization with a content database is the most important tool to the efficient use of search engines. A customer requesting information on a particular subject or item would otherwise have to search through hundred of results to find the most relevant information to his query. Hundreds of results through use of mining text are reduced by this step. This eliminates the aggravation and improves the navigation of information on the Web.
Keywords:
Information retrieval , text mining, web mining , web search engine,
References
-
Ai, Y., R. Gerling, M. Neumann, C. Nitschke and P. Riehmann, 2005. TIRA: Text based information retrieval architecture. Proceeding of the 2nd International Workshop on Text-based Information Retrieval, pp: 345-352.
-
Barathi, M. and S. Valli, 2011. Context disambiguation based semantic web search for effective information retrieval. J. Comput. Sci., 7(4): 548-553.
CrossRef -
Preethi, M., 2014. Concept based ontology matching by concept enrichment. Proceeding of International Conference on Global Innovations in Computing Technology (ICGICT’14), pp: 234-243.
-
Sagayam, R., S. Srinivasan and S. Roshni, 2012. A survey of text mining: Retrieval, extraction and indexing techniques. Int. J. Comput. Eng. Res., 2(5): 1443-1444.
-
Vasumathi, B. and S. Moorthi, 2012. Implementation of hybrid ANN-PSO algorithm on FPGA for harmonic estimation. Eng. Appl. Artif. Intel., 25: 476-483.
CrossRef -
Wang, Y.F., S. Cheng and M.H. Hsu, 2011. Incorporating the Markova chain concept into fuzzy stochastic prediction of stock indexes. Appl. Soft Comput., 10(2): 613-617.
CrossRef
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
|
|
![](http://www.maxwellsci.com/images/RJASET-maxw.jpg) |
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
![Submit Manuscript](../images/sub.jpg) |
Information |
|
|
|
Sales & Services |
|
|
|