A Survey of Sentiment Analysis Process and Technologies
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2019 by IJETT Journal|
|Year of Publication : 2019|
|Authors : Priyanka Namdev, Prof. Lakhan Singh
|DOI : 10.14445/22315381/IJETT-V67I11P225|
MLA Style: Priyanka Namdev, Prof. Lakhan Singh "A Survey of Sentiment Analysis Process and Technologies" International Journal of Engineering Trends and Technology 67.11 (2019):153-156.
APA Style:Priyanka Namdev, Prof. Lakhan Singh. A Survey of Sentiment Analysis Process and Technologies International Journal of Engineering Trends and Technology, 67(11),153-156.
Sentiment analysis of social networking is a newly rising research area of computer science which has recently attracted many researchers. Social networking like Twitters and Facebook present platforms for users where they can bring out, issue and publish their opinions and thoughts. In terms of thoughts and opinions expressed by the users, sentiment analysis undertake the problem by analyzing the text mining process. In this paper, an analytical survey is presented for sentiments analysis of social networking in context with methods and technologies. Finally, a concluding scope of sentiment analysis is presented for future research trends and its relative subject areas.
 B. Liu, Sentiment Analysis and Opinion Mining. Morgan & Claypool, 2012.
 R. Biagioni, Sentiment Analysis. Cham: Springer International Publishing, 2016, pp. 7–16.
 M. Bouazizi and T. Ohtsuki, “A pattern-based approach for multi-class sentiment analysis in twitter,” IEEE Access, vol. 5, pp. 20 617–20 639, 2017.
 D. Gonzalez-Marron, D. Mejia-Guzman, and A. Enciso- Gonzalez, “Exploiting data of the twitter social network using sentiment analysis,” in Applications for Future Internet, E. Sucar, O. Mayora, and E. Munoz de Cote, Eds. Cham: Springer International Publishing, 2017, pp. 35–38.
 C. Wang, Z. Xiao, Y. Liu, Y. Xu, A. Zhou, and K. Zhang, “Sentiview: Sentiment analysis and visualization for internet popular topics,” IEEE Transactions on Human-Machine Systems, vol. 43, no. 6, pp. 620–630, Nov 2013.
 A. Trilla and F. Alias, “Sentence-based sentiment analysis for expressive text-to-speech,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, no. 2, pp. 223–233, Feb 2013.
 M. Brooks, J. J. Robinson, M. K. Torkildson, S. R. Hong, and C. R. Aragon, “Collaborative visual analysis of sentiment in twitter events,” in Cooperative Design, Visualization, and Engineering, Y. Luo, Ed. Cham: Springer International Publishing, 2014, pp. 1–8.
 L. C. Yu, J. Wang, K. R. Lai, and X. Zhang, “Refining word embeddings using intensity scores for sentiment analysis,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 3, pp. 671–681, March 2018.
 M. Trupthi, S. Pabboju, and G. Narasimha, “Sentiment analysis on twitter using streaming api,” in 2017 IEEE 7th International Advance Computing Conference (IACC), Jan 2017, pp. 915– 919.
 F. Colace, L. Casaburi, M. D. Santo, and L. Greco, “Sentiment detection in social networks and in collaborative learning environments,” Computers in Human Behavior, vol. 51, pp. 1061 – 1067, 2015, computing for Human Learning, Behaviour and Collaboration in the Social and Mobile Networks Era.
 Z. Jianqiang and G. Xiaolin, “Comparison research on text preprocessing methods on twitter sentiment analysis,” IEEE Access, vol. 5, pp. 2870–2879, 2017.
 K. Schouten and F. Frasincar, “Survey on aspect-level sentiment analysis,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 3, pp. 813–830, March 2016.
 M. Z. Asghar, A. Khan, F. Khan, and F. M. Kundi, “Rift: A rule induction framework for twitter sentiment analysis,” Arabian Journal for Science and Engineering, vol. 43, no. 2, pp. 857– 877, Feb 2018.
 N. O¨ ztu¨rk and S. Ayvaz, “Sentiment analysis on twitter: A text mining approach to the syrian refugee crisis,” Telematics and Informatics, vol. 35, no. 1, pp. 136 – 147, 2018.
 C. Diamantini, A. Mircoli, D. Potena, and E. Storti, “Social information discovery enhanced by sentiment analysis techniques,” Future Generation Computer Systems, 2018.
 P. Goldar, Y. Rai, and S. Kushwaha, “A review on parallelization of big data analysis and processing,” International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE), vol. 23, no. 4, pp. 60–65, August 2016. [Online]. Available: http://www.ijetcse.com/wpcontent/ plugins/ijetcse/file/upload/docx/151A-Review-on- Parallelization-of-Big-Data-Analysis-and-Processing-pdf.pdf
 S. Rani and P. Kumar, “A sentiment analysis system to improve teaching and learning,” Computer, vol. 50, no. 5, pp. 36–43, May 2017.
 A. Hogenboom, F. Frasincar, F. de Jong, and U. Kaymak, “Using rhetorical structure in sentiment analysis,” Commun. ACM, vol. 58, no. 7, pp. 69–77, Jun. 2015.
Sentiment Analysis, Social Networking, Data Mining