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Study of Twitter Sentiment Analysis using Machine Learning Algorithms on Python

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

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

	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 = {},
	doi = {10.5120/ijca2017914022},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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.


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