Intelligence Approaches for Sentimental Analysis in Social Networks : A Survey

Authors(2) :-Karthik R. V., S. Ganapathy

Social network occupies an important place and also takes a considerable amount of time in people's daily lives. It has become so popular that people are sharing a huge amount of data and opinion on social network/review sites which in turn helps to find interesting insights for organizations/ vendors or consumers. In this Survey paper, we have presented the researches done in the area of Sentimental analysis / Opinion mining from Social network data. We also documented the outcome of comparative analysis, followed by our future work in Online product recommendations with help of Sentiment analysis.

Authors and Affiliations

Karthik R. V.
School of Computing Science and Engineering, VIT University-Chennai Campus, Chennai, India
S. Ganapathy
School of Computing Science and Engineering, VIT University-Chennai Campus, Chennai, India

Social Network, Sentiment Analysis, User Reviews, Recommendation, Opinion.

  1. Francesco Corea,"Can Twitter Proxy the Investors' Sentiment? The Case for the Technology Sector", Big Data Research, Vol. 4, pp. 70-74, 2016.
  2. Quan Fang, Changsheng Xu, Jitao sang, Shamim Hossain M and Ghulam Muhummad, "Word of mouth understanding: entity centric multimodal aspect opining mining in Social media", IEEE Transactions on Multimedia, Vol.17, No.12, pp. 411-427, July 2016.
  3. Xinjie Zhou, Xiaojun wan and Jianguo Xiao, "CMiner - Opinion extraction and summarization for Chinese microblogs", IEEE Transactions on Knowledge and Data Engineering", Vol. 28, pp. 7, July 2016.
  4. Sanjiv R Das and Mike Y Chen, "Yahoo for Amazon - Sentimental extraction from small talk on the web", Management science, Vol.53, No.9, pp. 1375-1388, 2007.
  5. B. Agarwal, N. Mittal, "Prominent Feature Extraction for Sentiment Analysis", Springer International Publishing, Socio-Affective Computing, Vol. 2, pp. 21-45, 2015.
  6. Manizheh Ghaemi, Mohammad-Reza Feizi-Derakhshi, "Feature selection using Forest Optimization Algorithm", Pattern Recognition, Vol. 60, pp.121-129, 2016.
  7. V Bolon Canedo and I Porto Diaz, "A Framework of cost based feature selection", Pattern Generation, Vol. 47, pp. 2481-2489, 2014.
  8. Pawel Teisseyre, "Feature Ranking for multi label classification using Markov Networks", Neuro computing, Vol. 205, pp.439-454, 2016.
  9. K. Zhang, R. Narayanan, A. Chaudhary, "Mining Online Customer Reviews For Ranking Products", EECS Department, Northwestern University, Technical Report, 2000.
  10. K. Zhang, R. Narayanan, A. Chaudhary, "Voice of the customers: mining online customer reviews for product feature-based ranking", WOSN'10, Boston, USA, 2010.
  11. K. Zhang, Y. Cheng, W.K. Liao, A. Chaudhary, "Mining millions of reviews: a technique to rank products based on importance of reviews", Proceedings of the 13th International Conference on Electronic Commerce, ACM, New York, USA, Article No.12, 2011.
  12. Yang Liu, Jian -Wu and Zhi-Ping Fan, "Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory", Information Fusion, pp. 149-161
  13. Jinpeng Wang, Wayne Xin Zhao, Yulan He and Xiaoming Li, "Leveraging product adapter information from Online reviews for product recommendation", AAAI Conference on web and social media, pp. 464-472, 2015.
  14. Rui Xia, "Dual Sentiment Analysis", Information Fusion, 2015.
  15. Cagatay Catal, Mehmet Nangir, "Sentiment classification model based on multiple classifier ", Applied Soft Computing, Vol. 50, pp. 135-141, November 2016.
  16. Matthijis Meire, Michel Ballings and Dirik Van den Poel, "The added Value of auxiliary data in Sentiment analysis of Facebook posts", Decision Support Systems, Vol. 89, pp. 98-113, 2016.
  17. Vimal Kumar B. Vaghela and Bhumika M. Jadav, "Analysis of Various Sentiment Classification Techniques", International journal of Computer applications, Vol. 140, pp. 22-27, April 2016.
  18. Shehal A Mulay, Shrijeet J, Mohit R Shaha, Hrishikesh V Vibhute and Mahesh P, "Sentimental analysis and opinion mining with social networking - Predicting Box office collection of Movie", International journal of emerging research in management and technology, Vol.5, No.1, pp. 74-79, January 2016.
  19. Sudipta Deb Roy and Sankar Kumar Chakraborty, "Impact of Social Media / Social Networks on Education and life", Vol. I, No. I, pp. 141-147, February 2015.
  20. Thomas L Griffiths, Mark Steyvers, David M Blei and Joshua B Tenenbaum, "Integrating topics and syntax", In advance in neural information processing systems, MIT press, Vol. 17, pp. 537-544, 2005.
  21. Shengsheng Qian and Tiazhu zing, "Multi modal event topic model for social event analysis", IEEE Transactions on Multimedia, Vol. 18, No. 2, pp. 233 - 246, Feb 2016.
  22. Suke Li, Zhi Guan, Liyong Tang and Zhong Chen,"Exploiting Consumer Reviews for Product Feature Ranking", Journal of Computer Science and Technology, Vol. 27, No.3, pp. 635-649, July 2012.
  23. I R Jayasekara and W M J I Wijayanayake, "Opinion mining of customer reviews: Feature and Simile based approach", International journal of Data mining & Knowledge Management process (IJKDP), Vol.6, No.1, pp.1-11, January 2016.
  24. Olena Kummer,"Feature Selection in Sentiment Analysis", CORIA 2012, Bordeaux, Page No. 273-284
  25. Anuj Sharma and Shubhmaoy Dey, "Performance Investigation of Feature Selection Methods and Sentiment Lexicons for Sentiment Analysis", Special issue of International Journal of Computer applications, pp. 15 - 20, June 2012.
  26. Gautami Tripati and Naganna, "Feature Selection and Classification approach for Sentiment Analysis", International Journal of MILAIJ, Vol. 2, No. 2, pp. 1-12, June 2015.

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Publication Details

Published in : Volume 3 | Issue 1 | January-February 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 1625-1631
Manuscript Number : CSEIT1831363
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Karthik R. V., S. Ganapathy, "Intelligence Approaches for Sentimental Analysis in Social Networks : A Survey", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.1625-1631, January-February-2018. |          | BibTeX | RIS | CSV

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