Identifying Human Personalized Sentiment with Streaming Data

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 160 - Number 7
Year of Publication: 2017
Authors:
F. M. Tanvir Hossain, Maruf Ahmed, Anik Saha, Khandaker Tabin Hasan
10.5120/ijca2017913088

Tanvir F M Hossain, Maruf Ahmed, Anik Saha and Khandaker Tabin Hasan. Identifying Human Personalized Sentiment with Streaming Data. International Journal of Computer Applications 160(7):26-31, February 2017. BibTeX

@article{10.5120/ijca2017913088,
	author = {F. M. Tanvir Hossain and Maruf Ahmed and Anik Saha and Khandaker Tabin Hasan},
	title = {Identifying Human Personalized Sentiment with Streaming Data},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2017},
	volume = {160},
	number = {7},
	month = {Feb},
	year = {2017},
	issn = {0975-8887},
	pages = {26-31},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume160/number7/27087-2017913088},
	doi = {10.5120/ijca2017913088},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Nowadays, social networks are becoming common platform of our emotion, sentiment, personality, and so on. A significant number of studies are also available about sentiment and emotion analysis from social network data. We observe that there are few studies are available those compute sentiment over real time data in Twitter and Foursquare. In this paper, we have conducted a research that can compute sentiment from real time data in a social network. We also use multiple techniques to compute sentiment such as sentiwordnet and textblob. We analyze the sentiments of a human from his/her twitter and from the location in foursquare of that person.

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Keywords

Big Data, Sentiment Analysis, LBSN, Social Network ,Hadoop .