Hybrid Semantic Analysis of Tweets: A Case Study of Tweets on Girl-Child in India

Authors

  • M. Madhukar IBM India Pvt Ltd, Bangalore, India
  • S. Verma Banasthali University, Rajasthan, India
Volume: 7 | Issue: 5 | Pages: 2014-2016 | October 2017 | https://doi.org/10.48084/etasr.1246

Abstract

Social networks have become one of the major and important parts of daily life. Besides sharing ones views the social networking sites can also be very efficiently used to judge the behavior and attitude of individuals towards the posts. Analysis of the mood of public on a particular social issue can be judged by several methods. Analysis of the society mood towards any particular news in form of tweets is investigated in this paper. The key objective behind this research is to increase the accuracy and effectiveness of the classification by the process of Natural Language Processing (NLP) Techniques while focusing on semantics and World Sense Disambiguation. The process of classification includes the combination of the effect of various independent classifiers on one particular classification problem. The data that is available in the form of tweets on twitter can easily frame the insight of the public attitude towards the particular tweet. The proposed work implements a hybrid method that includes Hybrid K, clustering and boosting. A comparison of this scheme versus a K-means/SVM approach is provided. Results are shown and discussed.

Keywords:

natural language processing (NLP), sentiment analysis, social networking analysis, social networking sites (SNS)

Downloads

Download data is not yet available.

References

A. Shaikh, T. Pritam, P. Ankita, W. Shital, T. Pooja, “Stock Exchange Market Prediction”, International Journal of Advances in Computer Science and Technology, Vol. 3, No. 5, pp. 349-351, 2014

A. Joshi, A. R. Balamurali, P. Bhattacharyya, R. Mohanty, “C-Feel-It: A Sentiment Analyzer for Micro-blog”, HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Systems Demonstrations, pp. 127-132, Portland, Oregon, June 21, 2011

A. Abbasi, “Intelligent feature selection for opinion classification”, IEEE Intelligent Systems, Vol. 25, No. 4, pp. 75-79, 2010

S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, 1998

D. Anguita, A. Ghio, N. Greco, L. Oneto, S. Ridella, “Model Selection for Support Vector Machines: Advantages and Disadvantages of the Machine Learning Theory”, Proc. of the International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, 2010 DOI: https://doi.org/10.1109/IJCNN.2010.5596450

D. Rajput, M. Mdhukar, S. Verma, M. Sharma, “Sentiment Analysis on Big Data using Machine Learning for Holiday Destinations”, 2015 IEEE European Modelling Symposium , Spain, October 6-8, 2015

A. Kumar, T. Mary, “Sentiment Analysis: A Perspective on its Past, Present and Future”, International Journal of Intelligent Systems and Applications, Vol. 10, pp.1-14, 2012 DOI: https://doi.org/10.5815/ijisa.2012.10.01

A. Somla, S. V. N. Vishwanathan, Introduction to Machine Learning, Cambridge University Press, 2009

A. M. Kaplan, M. Haenlein, “Users of the World, Unite! The Challenges and Opportunities of Social Media”, Business Horizons, Vol. 5, No. 1, pp. 59-68, 2010 DOI: https://doi.org/10.1016/j.bushor.2009.09.003

N. Anitha, B. Anitha, S. Pradeepa, “Sentiment Classification Approaches”, International Journal of Innovation Engineering and Technology, Vol. 3, No. 1, pp. 22-31, 2013

B. J. Jansen, M. Zhang, K. Sobel, A. Chowdury, “Twitter Power: Tweets as Electronic Word of Mouth”, Journal of The American Society for Information Science and Technology, Vol. 60, No. 11,pp. 2169–2188, 2009 DOI: https://doi.org/10.1002/asi.21149

B. Pang, L. Lee, S. Vaithyanathan, “Thumbs up? Sentiment Classification using Machine Learning Techniques”, Proceedings of the Conference on Empirical Methods in Natural Language Processing, Philadelphia, pp. 77-86, 2002 DOI: https://doi.org/10.3115/1118693.1118704

C. Vicient, A. Moreno, “Unsupervised Topic Discovery in micro-blogging networks”, Expert Systems with Applications, Vol. 42, pp. 6472–6485, 2015 DOI: https://doi.org/10.1016/j.eswa.2015.04.014

S. Verma, M. Sharma, D. Rajput, M. Mdhukar , V. Mittal, R.Singh, “Disclosing Tweet Polarity using feature representation factor”, International Journal Of Latest Trends In Engineering and Technology, Vol. 5, No. 2, 2015

Downloads

How to Cite

[1]
M. Madhukar and S. Verma, “Hybrid Semantic Analysis of Tweets: A Case Study of Tweets on Girl-Child in India”, Eng. Technol. Appl. Sci. Res., vol. 7, no. 5, pp. 2014–2016, Oct. 2017.

Metrics

Abstract Views: 475
PDF Downloads: 300

Metrics Information
Bookmark and Share