Call for Paper - November 2022 Edition
IJCA solicits original research papers for the November 2022 Edition. Last date of manuscript submission is October 20, 2022. Read More

Identifying Human Personalized Sentiment with Streaming Data

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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.

References

  1. A. Katrekar, “An introduction to sentiment analysis," GlobalLogic Inc., June 2005.
  2. G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions.," IEEE Trans. on Knowl. and Data Eng., 17(6):734749,, June 2005.
  3. P. W. Zhigao Zheng, J. Liu3, and S. Sun, “real-time big data processing framework: Challenges and solutions, applied mathematics & information sciences an international journal," An International Journal, 2015.
  4. M. Barlow, “Real-time big data analytics: Emerging architecture,” 1st ED. LONDON: OReilly Media., 2013.
  5. S. Loria, “Textblob python library or sentiment analysis,” sloria/TextBlob on GitHub at commit eb08c12“Twitter via sms faq,” April 13, 2012.
  6. Springer-Verlag, “A. bifet and e. frank. sentiment knowledge discovery in twitter streaming data.,” In DS10, pages 115, Berlin, Heidelberg,, 2010.
  7. H. M. J. Bollen and A. Pepe, “Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena.,” In ICWSM 11, 2011.
  8. B. Liu, “Sentiment analysis and opinion mining, morgan and claypool publishers,” Morgan and Claypool Publishers, 2012.
  9. B. R. D. K. A. M. Michael Wiegand, Alexandra Balahur, “Asurvey on the role of negation in sentiment analysis.,” Proceedings of the workshop on negation speculation in natural language processing 6068, Association for Computational Linguistics., 2010.
  10. T.-C. Peng and C.-C. Shih, “An unsupervised snippet-based sentiment classifi- cation method for chinese unknown phrases without using reference word pairs,” IEEE/WIC/ACM International Conference on Web Intelligence and intelligent Agent Technology journal of computer, volume 2, issue 8, august 2010, issn 2151- 9617 ., 2010.
  11. D. Hardt, “The oauth 2.0 authorization framework,,” Internet Engineering Task Force (IETF), Request for Comments: 6749, Obsoletes: 5849 ,Category: Standards Track, ISSN: 2070-1721,, 2012.
  12. “Froursquare library for python, developed for manipulating and retrieving froursquare data,” https://github.com/mLewisLogic/foursquare.
  13. “Tweepy library for python, developed for manipulating and retrieving twitter data,,” http://www.tweepy.org/
  14. “Hadoop, powered by hadoop,”
  15. “What is hadoop,” 2016.
  16. “Hadoop vs traditional database management system,” 08-aug-2016.
  17. Mukta, Md Saddam Hossain, Mohammed Eunus Ali, and Jalal Mahmud. "User Generated vs. Supported Contents: Which One Can Better Predict Basic Human Values?." International Conference on Social Informatics. Springer International Publishing, 2016.
  18. Mukta, Md Saddam Hossain, Mohammed Eunus Ali, and Jalal Mahmud. "Identifying and validating personality traits-based homophilies for an egocentric network." Social Network Analysis and Mining 6.1 (2016): 74.
  19. Rahman, Md Mahabur, et al. "Can we predict eat-out preference of a person from tweets?." Proceedings of the 8th ACM Conference on Web Science. ACM, 2016.
  20. Giunchiglia, Fausto, et al. "Semantic enabled role based social network." International Journal of Intelligent Systems and Applications 4.12 (2012): 1.
  21. Hasan, Khandaker Tabin, et al. "Event-based content management by spontaneous metadata generation and diffusion." Computational Intelligence and Informatics (CINTI), 2012 IEEE 13th International Symposium on. IEEE, 2012.

Keywords

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