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Study of Twitter Sentiment Analysis using Machine Learning Algorithms on Python

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Bhumika Gupta, Monika Negi, Kanika Vishwakarma, Goldi Rawat, Priyanka Badhani
10.5120/ijca2017914022

Bhumika Gupta, Monika Negi, Kanika Vishwakarma, Goldi Rawat and Priyanka Badhani. Study of Twitter Sentiment Analysis using Machine Learning Algorithms on Python. International Journal of Computer Applications 165(9):29-34, May 2017. BibTeX

@article{10.5120/ijca2017914022,
	author = {Bhumika Gupta and Monika Negi and Kanika Vishwakarma and Goldi Rawat and Priyanka Badhani},
	title = {Study of Twitter Sentiment Analysis using Machine Learning Algorithms on Python},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2017},
	volume = {165},
	number = {9},
	month = {May},
	year = {2017},
	issn = {0975-8887},
	pages = {29-34},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume165/number9/27604-2017914022},
	doi = {10.5120/ijca2017914022},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Twitter is a platform widely used by people to express their opinions and display sentiments on different occasions. Sentiment analysis is an approach to analyze data and retrieve sentiment that it embodies. Twitter sentiment analysis is an application of sentiment analysis on data from Twitter (tweets), in order to extract sentiments conveyed by the user. In the past decades, the research in this field has consistently grown. The reason behind this is the challenging format of the tweets which makes the processing difficult. The tweet format is very small which generates a whole new dimension of problems like use of slang, abbreviations etc. In this paper, we aim to review some papers regarding research in sentiment analysis on Twitter, describing the methodologies adopted and models applied, along with describing a generalized Python based approach.

References

  1. David Zimbra, M. Ghiassi and Sean Lee, “Brand-Related Twitter Sentiment Analysis using Feature Engineering and the Dynamic Architecture for Artificial Neural Networks”, IEEE 1530-1605, 2016.
  2. Varsha Sahayak, Vijaya Shete and Apashabi Pathan, “Sentiment Analysis on Twitter Data”, (IJIRAE) ISSN: 2349-2163, January 2015.
  3. Peiman Barnaghi, John G. Breslin and Parsa Ghaffari, “Opinion Mining and Sentiment Polarity on Twitter and Correlation between Events and Sentiment”, 2016 IEEE Second International Conference on Big Data Computing Service and Applications.
  4. Mondher Bouazizi and Tomoaki Ohtsuki, “Sentiment Analysis: from Binary to Multi-Class Classification”, IEEE ICC 2016 SAC Social Networking, ISBN 978-1-4799-6664-6.
  5. Nehal Mamgain, Ekta Mehta, Ankush Mittal and Gaurav Bhatt, “Sentiment Analysis of Top Colleges in India Using Twitter Data”, (IEEE) ISBN -978-1-5090-0082-1, 2016.
  6. Halima Banu S and S Chitrakala, “Trending Topic Analysis Using Novel Sub Topic Detection Model”, (IEEE) ISBN- 978-1-4673-9745-2, 2016.
  7. Shi Yuan, Junjie Wu, Lihong Wang and Qing Wang, “A Hybrid Method for Multi-class Sentiment Analysis of Micro-blogs”, ISBN- 978-1-5090-2842-9, 2016.
  8. Apoorv Agarwal, Boyi Xie, Ilia Vovsha, Owen Rambow and Rebecca Passonneau, “Sentiment Analysis of Twitter Data” Proceedings of the Workshop on Language in Social Media (LSM 2011), 2011.
  9. Neethu M S and Rajasree R, “Sentiment Analysis in Twitter using Machine Learning Techniques”, IEEE – 31661, 4th ICCCNT 2013.
  10. Aliza Sarlan, Chayanit Nadam and Shuib Basri, “Twitter Sentiment Analysis”, 2014 International Conference on Information Technology and Multimedia (ICIMU), Putrajaya, Malaysia November 18 – 20, 2014.
  11. Feature engineering, Wikipedia 2017, https://en.wikipedia.org/wiki/Feature_engineering

Keywords

Sentiment analysis, Machine Learning, Natural Language Processing, Python.