CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Studying the Complex Inter-relationships amongst various SNA Metrics and SEA Metrics using ISM Methodology

by Yogender Singh, Remica Aggarwal, Lakshay Aggarwal, V. K. Aggarwal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 30
Year of Publication: 2020
Authors: Yogender Singh, Remica Aggarwal, Lakshay Aggarwal, V. K. Aggarwal
10.5120/ijca2020920342

Yogender Singh, Remica Aggarwal, Lakshay Aggarwal, V. K. Aggarwal . Studying the Complex Inter-relationships amongst various SNA Metrics and SEA Metrics using ISM Methodology. International Journal of Computer Applications. 176, 30 ( Jun 2020), 28-35. DOI=10.5120/ijca2020920342

@article{ 10.5120/ijca2020920342,
author = { Yogender Singh, Remica Aggarwal, Lakshay Aggarwal, V. K. Aggarwal },
title = { Studying the Complex Inter-relationships amongst various SNA Metrics and SEA Metrics using ISM Methodology },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2020 },
volume = { 176 },
number = { 30 },
month = { Jun },
year = { 2020 },
issn = { 0975-8887 },
pages = { 28-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number30/31394-2020920342/ },
doi = { 10.5120/ijca2020920342 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:43:53.875474+05:30
%A Yogender Singh
%A Remica Aggarwal
%A Lakshay Aggarwal
%A V. K. Aggarwal
%T Studying the Complex Inter-relationships amongst various SNA Metrics and SEA Metrics using ISM Methodology
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 30
%P 28-35
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Social Network Analysis is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes which includes individual actors, people, or things within the network and the ties, edges, or links which include relationships or interactions that connect them. Examples include friendship and acquaintance networks , business networks , difficult working relationships, knowledge networks , diseases transmissions , sexual relationships etc. On the other hand, sentiment analysis helps in determining the emotional temperament of reviewers and writers and helps to identify, extract or portray intuitive information, such as opinions, which may be expressed in a certain given piece of text or topic. Present research work attempts to explore the various metrics associated with the success of social media network analysis as well as sentiment analysis and thereafter it tries to establish the inter-relationships between the various sentiment analysis metrics through ISM methodology.

References
  1. Otte, E. and Rousseau, R. 2002. Social network analysis: a powerful strategy, also for the information sciences. Journal of Information Science. 28 (6): 441–453. doi:10.1177/016555150202800601. Retrieved 2015-03-23.
  2. Hagen. L.; Neely, S., ; Robert-Cooperman, C., Keller ,T. and DePaula, N. 2018. Crisis Communications in the Age of Social Media: A Network Analysis of Zika-Related Tweets . Soc. Sci. Comput. Rev. Social Science Computer Review. 36 (5): 523–541. ISSN 0894-4393. OCLC 7323548177.
  3. Ghanbarnejad, Fakhteh, Saha Roy, Rishiraj, Karimi, Fariba, Delvenne, Jean-Charles, Mitra, Bivas 2019. Dynamics on and of Complex Networks III Machine Learning and Statistical Physics Approaches. Cham: Springer International Publishing : Imprint: Springer. ISBN 9783030146832. OCLC 1115074203.
  4. Anheier, H.K.; Gerhards, J.; Romo, F.P.1995. "Forms of capital and social structure of fields: examining Bourdieu's social topography". American Journal of Sociology.
  5. De Nooy, W. 2003. Fields and networks: Correspondence analysis and social network analysis in the framework of Field Theory. Poetics. 31 (5–6): 305–27. doi:10.1016/s0304-422x(03)00035-4.
  6. Sudhahar S, De Fazio G, Franzosi R, Cristianini N 2013. Network analysis of narrative content in large corporation. Natural Language Engineering. 21 (1): 1–32. doi:10.1017/S1351324913000247.
  7. Esuli, A., Sebastiani, F., Baccianella, S. 2010. Senti Word Net 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC 2010), Valletta, Malta.
  8. Gryc, W., Moilanen, K. 2010. Leveraging Textual Sentiment Analysis with Social Network Modeling: Sentiment analysis of political blogs in the 2008 U.S. presidential election. In: Proceedings of the From Text to Political Positions Workshop, Vrije Universiteit, Amsterdam.
  9. Pang, B., Lee, L. 2008. Opinion Mining and Sentiment Analysis. Foundations Trends Inf. Retrieval 2(1-2), 1–135.
  10. Tan, S., Zhang, J. 2008. An empirical study of sentiment analysis for Chinese documents. Expert Systems with Applications 34(4), 2622–2629 .
  11. Zhe Xu, B.S. 2010. A Sentiment Analysis Model Integrating Multiple Algorithms and Diverse Features. M.Sc. Thesis, Ohio State University.
  12. Onder Coben , Bans Ozyer and Gushah Tumueo Ozyer . INSPEC Accession Number : 15986196. A Comparison of Similarity Metrics for Sentiment Analysis on Turkish Twitter feeds 2015 IEEE International conference smart city social com / sustain com (Smart City ).
  13. Shams, M., Saffar, M., Shakery, A., Faili, H. 2012. Applying Sentiment and Social Network Analysis in User Modeling. In: Gelbukh A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2012. Lecture Notes in Computer Science, Vol. 7181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28604-9_43. ISBN 978-3-642-28603-2.
  14. Boiy, E., Moens, M.F. 2009. A machine learning approach to sentiment analysis in multilingual web texts. Information Retrieval 12(5), 526–558 .
  15. Bontcheva, K., Derczynski, L., Funk, A., Greenwood, M.A., Maynard, D., Aswani, N.: Twitie 2013. An Open-Source Information Extraction Pipeline for Microblog Text. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing. Association for Computational Linguistics .
  16. Hare, J.S., Lewis, P.H., Enser, P.G.B., Sandom, C.J. 2006. A linear-algebraic technique with an application in semantic image retrieval. In: Sundaram, H., Naphade, M.R., Smith, J.R., Rui, Y. (eds.) CIVR. Lecture Notes in Computer Science, vol. 4071, pp. 31–40. Springer.
  17. Hare, J.S., Samangooei, S., Dupplaw, D.P. Open IMAJ and Image Terrier 2011. Java libraries and tools for scalable multimedia analysis and indexing of images. In: Proceedings of the 19th ACM International Conference on Multimedia. pp. 691–694. ACM, New York, NY, USA.
  18. Flora, P. , Claus, E., Christine, S. 2018. Social media metrics and sentiment analysis to evaluate the effectiveness of social media posts . 9th international conference on ambient systems , networks and technologies , ANT-2018 and the 8th international conference on sustainable energy and information technology, Porto, Portugal. Procedia computer science 130, 660-666.
  19. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M. 2011. Lexicon-based methods for sentiment analysis. Computational Linguistics , 1–41.
  20. Weichselbraun, A., Gindl, S., Scharl, A. 2010. A context-dependent supervised learning approach to sentiment detection in large textual databases. Journal of Information and Data Management 1(3), 329–342.
  21. Warfield, J.N. 1974. Developing interconnection matrices in structural modelling , in the proceedings of IEEE Transactions on System, Man, and Cybernetics, SMC, 4 (1), 81-87.
Index Terms

Computer Science
Information Sciences

Keywords

Social Media Metrics Social Network Analysis Sentiment Analysis ISM Methodology SNAM SeAM

Powered by PhDFocusTM