Content Analysis in Social Network Analysis using Sentiment Analysis

Authors

  • M. Thangaraj  Department of Computer Science, Madurai Kamaraj University, Madurai, Tamilnadu, India
  • S. Amutha  Department of Computer Science, Manonmanaiam Sundaranar University, Tirunelveli, Tamilnadu, India

Keywords:

Social network Analysis, Sentiment Analysis, Sentiment Annotation, Content Analysis, Sentiment Classification

Abstract

This paper shows about content from social network tools sites or social media tools like twitter or face book user document are analyzed with the help of Social network Analysis tools like Gephi, NodeXL and Pajek using positive, negative, neutral and emoticons. In Sentiment Analysis the text are classified in to various levels like word, sentence, phrase, and feature and document level. The content mining is divided into five categories. The major analysis is Sentiment Annotation, Sentiment Classification, Sentiment Detection, Sentiment Determination, Sentiment Extraction and Sentiment Lexicon.

References

  1. Md. Abdur Rahman & Heung-Nam Kim &Abdulmotaleb El Saddik &Wail Gueaieb, A context-aware multimedia framework toward personal social network services, International Journal in Multimedia Tools Applications (2014) 71:1717–1747
  2. Ahmed Al-Dhanhani, Rabeb Mizouni, Hadi Otrok, Ahmad Al- Rubaie, Analysis of collaborative learning in social network sites used in education, International journal of Social Network Analysis and Mining. (2015) 5:65
  3. Anuradha Goswami, Ajey Kumar, Challenges in the Analysis of Online Social Networks: A Data Collection Tool Perspective, Wireless Personal Communications 18 August 2017,97:4015–4061
  4. Cheng-Chung Chen Xiaoxi Fu Che-Yuan Chang, A terms mining and clustering technique for surveying network and content analysis of academic groups exploration, International Journal of Cluster Computing (2017) 20:43–52
  5. E. Fersini1 F. A. Pozzi1 E. Messina1, Approval network: a novel approach for sentiment analysis in social networks, International journal of World Wide Web (2017) 20:831–854
  6. Goran Putnik, Eric Cost, Catia Alves, Helio Castro, Leonilde Varela, Vaibhav Shah, Analysing the correlation between social network analysis measures and performance of students in social network-based engineering education, International Journal of Technology and Design Education, (2016) 26:413–437
  7. Jong-Soo Sohn Un-Bong Bae In-Jeong Chung, Contents Recommendation Method Using Social Network Analysis,international journal of Wireless Personal Communications (2013) 73:1529–1546
  8. Kawaljeet Kaur Kapoor & Kuttimani Tamilmani & Nripendra P. Rana2 & Pushp Patil &Yogesh K. Dwivedi & Sridhar Nerur , Advances in Social Media Research: Past, Present and Future, Information Systems Frontiers - Springer Nov 6, 2017
  9. S. Venkatesan, Vladimir A. Oleshchuk. C. Chellappan, Sourabh Prakash, Analysis of key management protocols for social networks, , International Journal of Social Network Analysis and Mining. (2016) 6:3
  10. Vishal A. Kharde, S.S. Sonawane,, Sentiment Analysis of Twitter Data: A Survey of Techniques, International Journal of Computer Applications (0975 – 8887) Volume 139 – No.11, April 2016

International Journal of Scientific Research in Science and Technology

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Published

2018-04-30

Issue

Volume 4, Issue 5, March-April-2018

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]
M. Thangaraj, S. Amutha, " Content Analysis in Social Network Analysis using Sentiment Analysis, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4, Issue 5, pp.1133-1137, March-April-2018.