Tweet Segmentation using Correlation and Association

Authors

  • Mr. Umesh A. Patil  HOD, Department of Computer Science & Engineering, D.Y.Patil Technical Campus, Talsande, Kolhapur, India.
  • Miss. Madhuri M. Pisotre  Student, BE (CSE), D.Y.Patil Technical Campus, Talsande, Kolhapur, IndiaFaculty of Environmental Studies, Universiti Putra Malaysia, UPM Serdang, Selangor Darul Ehsan
  • Miss. Snehal D. Gouraje  Student, BE (CSE), D.Y.Patil Technical Campus, Talsande, Kolhapur, IndiaFaculty of Environmental Studies, Universiti Putra Malaysia, UPM Serdang, Selangor Darul Ehsan
  • Miss. Ashwini P. Patil  Student, BE (CSE), D.Y.Patil Technical Campus, Talsande, Kolhapur, IndiaFaculty of Environmental Studies, Universiti Putra Malaysia, UPM Serdang, Selangor Darul Ehsan

Keywords:

Tweet dataset, Tweet segmentation, Microsoft N-gram, Correlation and Association

Abstract

Twitter is an online social network used by millions people. It used to provide a way to collect and understand user’s opinion about much private and public organization. Twitter has become one of the most important communication channels with it's achieve to providing the most up-to-date information to the user. In this paper we present to find the correlation of two words using the association rule. There must be an application to establish the mutual relationship between two words or sentences or segment. In the first step we collecting tweets are editable group of tweets hand selected by twitter user. These collected tweets are pre-processing in which stop words removed and then tweet segmentation. The form of generalized association rules, from messages posted by twitter users. The analysis of twitter post is focused on two different but related features: their textual content and their submission content. Due to it’s in valuable business value of timely information from these tweets, it is imperative to understand tweets language for a large body of downstream application, such as true named entity.

References

  1. D. Downey, M. Brodhead, and O. Etzioni. Locating complex named entities in web text. In Proc. of IJCAI, 2007.
  2. T. Finin, W. Murnane, A. Karandikar, N. Keller, J. Martineau, and M. Dredze. Annotating named entities in twitter data with crowd sourcing. In Proc. of the Workshop on Creating Speech and Language Data With Mechanical Turkat NAACL-HLT, 2010.
  3. K. Gimpel, N. Schneider, B. O’Connor, D. Das, D. Mills, J. Eisenstein, M. Heilman, D. Yogatama, J. Flannigan, and N. A. Smith. Part-of-speech tagging for twitter: Annotation, features, and experiments. In Proc. of ACL, 2011.
  4. B. Han and T. Baldwin. Lexical normalization of short text messages: Maknsens a #twitter. In Proc. of ACL, 2011.
  5. X. Liu, S. Zhang, F. Wei, and M. Zhou. Recognizing name identities in tweets. In Proc. of ACL, 2011.
  6. A. Ritter, S. Clark, Mausam, and O. Etzioni. Named entity recognition in tweets: An experimental study. In Proc. Of EMNLP, 2011.
  7. R. Agrawal, T. Imielinski, and A.N. Swami. Mining association rules between sets of items in large databases. In SIGMOD Conference, pages 207–216, 1993.
  8. W.A.V.B.D. Caragea and W.H. Hsu. Ontology-aware classification and association rule mining for interest and link prediction in social networks, 2009.

International Journal of Scientific Research in Science and Technology

Downloads

Published

2017-04-30

Issue

Volume 3, Issue 3, March-April-2017

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Mr. Umesh A. Patil, Miss. Madhuri M. Pisotre, Miss. Snehal D. Gouraje, Miss. Ashwini P. Patil "Tweet Segmentation using Correlation and Association" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 3, Issue 3, pp.390-394, March-April-2017.