Sentiment Analysis of Twitter User Data on Punjab Legislative Assembly Election, 2017

Sentiment Analysis of Twitter User Data on Punjab Legislative Assembly Election, 2017

Автор: Akhilesh Kumar Singh, Deepak Kumar Gupta, Raj Mohan Singh

Журнал: International Journal of Modern Education and Computer Science (IJMECS) @ijmecs

Статья в выпуске: 9 vol.9, 2017 года.

Бесплатный доступ

Sentiment Analysis is the way of gathering and inspecting data based on the personal emotions, reviews, and contemplations. The sentimental analysis is also recognized as opinion mining since it mines the major feature from people opinions. There are various social networking platforms, out of which Twitter is praised by lawmakers, academicians, and journalists for its potential political values. In literature, numerous studies have been performed on the data ecstatic to elections on Twitter. The greater part of them has been on the U.S Presidential Elections where there are two main applicants who fight it out. Since individuals discuss so many political parties and candidates and their prospects too in rendered messages, the issues of distinguishing their political feeling become extensive and fascinating. Consideration of all these aspects along with a sheer volume of data propelled us to look into the data and find interesting inferences in it. To select the 117 members of the Punjab Legislative Assembly, Legislative Assembly election was held in Punjab, the State of India on 4 February 2017. As per the Election Commission, a total of 1.9 crore voters is eligible for voting in August 2016 in Punjab. The data set that is collected with the help of Twython was analyzed to find out trivial things and interesting patterns in the data. The central idea of this research paper is to carry out the sentiment analysis on Legislative Assembly election 2017 that was held in the Punjab, a state of India for the Political Parties like BJP, INC, and AAP. We have analyzed and fetch significant implications from the tweets gathered over the whole period of elections.

Еще

Sentiment Analysis, Twython, Textblob, NLTK, Twitter, NLP

Короткий адрес: https://sciup.org/15015003

IDR: 15015003

Список литературы Sentiment Analysis of Twitter User Data on Punjab Legislative Assembly Election, 2017

  • H. Zang, “The optimality of Naïve-Bayes”, Proc. FLAIRS, 2004
  • C.D. Manning, P. Raghavan, and H. Schütze, "Introduction to Information Retrieval", Cambridge University Press, pp. 234-265, 2008
  • A. McCallum and K. Nigam, “A comparison of event models for Naive Bayes text classification”, Proc. AAAI/ICML-98 Workshop on Learning for Text Categorization, pp. 41-48, 1998
  • M. Schmidt, N. L. Roux, and F. Bach, "Minimizing Finite Sums with the Stochastic Average Gradient", 2002
  • Y. LeCun, L. Bottou, G. Orr and K. Muller, “Efficient BackProp”, Proc. In Neural Networks: Tricks of the trade 1998.
  • T. Wu, C. Lin and R. Weng, "Probability estimates for multiclass classification by pairwise coupling", Proc. JMLR-5, pp. 975-1005, 2004
  • “Support Vector Machines” [Online], http://scikitlearn.org/stable/modules/svm.html#svm-classification, Accessed Jan 2016
  • P. Pang, L. Lee and S. Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques”, Proc. ACL-02 conference on Empirical methods in natural language processing, vol.10, pp. 79-86, 2002
  • P. Pang and L. Lee, “Opinion Mining and Sentiment Analysis. Foundation and Trends in Information Retrieval”, vol. 2(1-2), pp.1-135, 2008
  • E. Loper and S. Bird, "NLTK: the Natural Language Toolkit", Proc. ACL-02 Workshop on Effective Tools and Methodologies for teaching natural language processing and computational linguistics, vol. 1,pp. 63-70, 2002
  • H. Wang, D. Can, F. Bar and S. Narayana, "A system for real-time Twitter sentiment analysis of 2012 U.S.presidential election cycle", Proc. ACL 2012 System Demonstration, pp. 115-120, 2012
  • O. Almatrafi, S. Parack, and B. Chavan, "Application of location-based sentiment analysis using Twitter for identifying trends towards Indian general elections 2014". Proc. The 9th International Conference on Ubiquitous Information Management and Communication, 2015
  • L. Jiang, M. Yu, M. Zhou, X. Liu and T. Zhao, "Target-dependent Twitter sentiment classification", Proc. The 49th Annual Meeting of the Association 39 for Computational Linguistics: Human Language Technologies, vol. 1, pp. 151-160, 2011
  • C. Tan, L. Lee, J. Tang, L. Jiang, M. Zhou and P. Li, “User-level sentiment analysis incorporating social networks”, Proc. The 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1397- 1405, 2011
  • A. Pak and P. Paroubek, “Twitter as a Corpus for Sentiment Analysis and Opinion Mining”, vol. 10, pp. 1320-1326, 2010
  • B. Sun and TY. V. Ng, "Analyzing the Sentimental influence of Posts on Social Networks", Proc. The 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design, 2014
  • A. Go, R. Bhayani and L. Huang, “Twitter sentiment classification using distant supervision”, CS224N Project Report, Stanford, vol.1-12, 2009
  • A. Barhan and A. Shakhomirov, “Methods for Sentiment Analysis of Twitter Messages”, Proc.12th Conference of FRUCT Association, 2012
  • T. Mitchell, "Machine Learning", McGraw-Hill, 1997 F. Jensen, "An Introduction to Bayesian Networks", Springer, 1996
  • T. C. Peng and C. C. Shih, “An Unsupervised Snippet-based Sentiment Classification Method for Chinese Unknown Phrases without using Reference Word Pairs”. IEEE/WIC/ACM Int. Conf. on Web Intelligence and Intelligent Agent Technology, vol. 3, pp. 243-248, 2010
  • R. Feldman, “Techniques and applications for sentiment analysis”, Proc.ACM, pp. 56-82, 2009
  • N. Cristianini and J. Shawe-Taylor, “An Introduction to Support Vector Machines and Other Kernel-based Learning Methods”, Cambridge University Press, March 2000
  • “An Introduction to Python”, v3.4.1, 2015 [Online], Available:https://docs.python.org
  • http://indiatoday.intoday.in/story/punjab-assembly-political-parties-socialmedia/1/869440.html
  • http://www.livemint.com/Industry/VThUq5I4BivpTDZdQb5sNN/Mobile-Internet-users-in-India-to-double-by-2017-says-study.html
  • Conference on Design Education (Connected 2010), July 2010, UNSW, Sydney, Australia.
  • E. Riloff, J. Wiebe, “Learning Extraction Patterns for Subjective Expressions” Proc. EMNLP-03 Conference on Empirical Methods in Natural Language Processing
  • P. Turney “Thumbs up or thumbs down?” Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp. 417424, 2002.
  • M. McLuhan, “The Medium is the Message” Understanding Media: The Extensions of Man,1964
  • Akshi Kumar, Teeja Mary Sebastian,"Sentiment Analysis: A Perspective on its Past, Present, and Future", IJISA, vol.4, no.10, pp.1-14, 2012. DOI: 10.5815/ijisa.2012.10.01
  • Lopamudra Dey, Sanjay Chakraborty, Anuraag Biswas, Beepa Bose, Sweta Tiwari,"Sentiment Analysis of Review Datasets Using Naïve Bayes' and K-NN Classifier", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.8, No.4, pp.54-62, 2016. DOI: 10.5815/ijieeb.2016.04.07
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp