Author : Navaneethakrishnan P 1
Date of Publication :12th October 2017
Abstract: In recent years, Big Data Analytics has become an essential topic for researchers.It gains more popularity due to immense data set that becomes overwhelming to users. Therefore itis essential to extract opinions from the internet and predict online customer’s preferences, which could prove valuable for economic or marketing research.This inspire the researcher to develop a system that can identify and classify opinion the huge amount of text data based on the approach of Sentiment Analysis or Opinion Mining. The paper presents a survey covering the techniques and methods in sentiment analysis and challenges appear in the field. Sentiment analysis is done in data from applications like social network. There is a need for analyzing the sentiments of data and thereby defining the behaviour of the user. This involves feature extraction and thereby developing relationship trees within the scope of data.
Reference :
-
- Zhi-Hong Deng , Kun-Hu Luo and Hong-Liang Yu(2014), “A study of supervised term weighting scheme for sentiment analysis”, Elsevier, Expert Systems with Applications ,Vol.41(7),PP. 3506–3513.
- Alvaro Ortigosa, José M. Martín and Rosa M. Carro(2014),” Sentiment analysis in Facebook and its application to e-learning”, Elsevier,Computers in Human Behavior ,Vol.31, PP.527–541.
- Li.T.,Zhu.S., &Ogihara.M. (2008), “Text ategorization via generalized discriminant analysis”, Information Processing & Management, 44(5), 1684-1697. 167
- Liang. J., Liu. P., Tan. J., & Bai. S. (2014), “Sentiment Classification Based on AS-LDA Model”, Procedia Computer Science, 31, 511-516.
- Liu.B.,&Zhang.L. (2012), “A survey of opinion mining and sentiment analysis”, In Mining Text Data, Springer US, 415- 463.
- Miao. Q., Li. Q., & Zeng. D. (2010), “Mining fine grained opinions by using probabilistic models and domain knowledge”, Proceedings of the 2010 IEEE/ WIC/ACM international conference on web intelligence and intelligent agent technology – WI-IAT’10 ,Washington, DC, USA: IEEE Computer Society 01, 358–365,.
- Min-Chul Yang and Hae-Chang Rim(2014), “Identifying interesting Twitter contents using topical analysis”, Expert Systems with Applications ,Elsevier,Vol.41(9),PP. 4330–4336
- Moraes. R., Valiati. J. F. &GaviãONeto. W. P. (2013), “Document-level sentiment classification: An empirical comparison between SVM and ANN”, Expert Systems with Applications, 40(2), 621-633.
- Nasukawa. T., & Yi. J. (2003), “Sentiment analysis: Capturing favorability using natural language processing”, In Proceedings of the 2nd international conference on Knowledge capture, ACM, 70-77.
- Xia.R.,Zong.C., Hu.X., &Cambria.E. (2013), “Feature ensemble plus sample selection: domain adaptation for sentiment classification”, Intelligent Systems, IEEE, 28(3), 10-18.