Earticle

초록

영어

The amount of digital information that is created and used is progressively rising along with the growth of sophisticated hardware and software. In addition, real-world data come in a diversity of forms and can be tremendously bulky. This has augmented the need for powerful algorithms that can deduce and dig out appealing facts and useful information from these data. Text Mining (TM), which is a very complex process; has been successfully used for this purpose. Text mining alternately referred to as text data mining, more or less equivalent to text analytics, can be defined as the process of extracting high-quality information from text. Text mining involves the process of structuring the input data, deriving patterns within the structured data and lastly interpretation and revelation of the output. This paper provides outline on text analytics and social media analytics. At the end, this paper presents our proposed work based on ontology framework to cope up with excessive social media textual data.

목차

Abstract
 1. Introduction
  1.1. Social Media Analytics
 2. Text Clustering
 3. Ontology Framework: An Approach for Data Retrieval
  3.1. Web Ontology Language
  3.2. Proposed Ontology Framework
  3.3. Ontology Classes
 4. Case Study: E-Tourism
 5. Conclusion and Future Work
 References

키워드

저자

• Pankajdeep Kaur [ GNDU, Regional Campus, GNDU, Regional Campus, GNDU, Regional Campus, Jalandhar, Jalandhar, Jalandhar ]
• Pallavi Sharma [ GNDU, Regional Campus, GNDU, Regional Campus, GNDU, Regional Campus, Jalandhar, Jalandhar, Jalandhar ]
• Nikhil Vohra [ GNDU, Regional Campus, GNDU, Regional Campus, GNDU, Regional Campus, Jalandhar, Jalandhar, Jalandhar ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605