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Microblogging Comments Classification

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Swapnil Babaji Shinde, Mohammad Muzammil Shaikh, Sudeep Thepade
10.5120/ijca2017914177

Swapnil Babaji Shinde, Mohammad Muzammil Shaikh and Sudeep Thepade. Microblogging Comments Classification. International Journal of Computer Applications 167(2):19-22, June 2017. BibTeX

@article{10.5120/ijca2017914177,
	author = {Swapnil Babaji Shinde and Mohammad Muzammil Shaikh and Sudeep Thepade},
	title = {Microblogging Comments Classification},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {167},
	number = {2},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {19-22},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume167/number2/27743-2017914177},
	doi = {10.5120/ijca2017914177},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Nowadays, microblogging sites like, Twitter, Pinterest is used by many people to share their sentiments. These comments can be classified and analyzed to find hidden patterns. The System needs to classify these comments into various classes which can be used to find the interest of users. These interests of users will be used for giving them personalized news and also for decision making in business. Twitter tweets having a limit of 140 characters. So, people share only important comments through tweets. Using text mining most important keywords can be found from tweets and classified accordingly in multiple classes.

References

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Keywords

Naïve Bayes Classification, Twitter Feeds Analysis, Text Mining, News Recommendation