Open Access Open Access  Restricted Access Subscription or Fee Access

Co-Extracting Opinion Targets and Opinion Words from Online Reviews Based On the Word Alignment Model

N. Krishnan, S. Sathyapriya, Dr. D. Anitha


The significant chore of estimation mining is to extract opinion targets and estimation vocabulary from a huge number of product reviews. The one of the come within reach of proposes a method base on partially supervised word arrangement representation, in which estimation relations naming is consider as an alignment process. Manipulative estimation relationship between words is an important for constructing Co-Ranking graph; to find self-assurance of each applicant graph based co-ranking algorithm is used. Higher confidence applicant are extracted as estimation targets or estimation words. Prior information also consider in finding confidence of applicant as being estimation target or estimation word. Previous methods are based on sentence structure based, compared to these methods proposed model minimizes negative effects of parsing errors. Due to use of partial supervision proposed model achieves better accuracy compared to unsupervised word alignment model. Final task is to extractive summary generation from estimation Targets and estimation Words with Word Alignment Model.


Estimation Mining, Estimation Target Extraction, Estimation Word Extraction, Text Mining

Full Text:



Agrawal V. P.; Financial Markets Operations; Sahitya Bhawan Publications; Ed. 2005, p. 239-240.

Assel K.R.Icfai Reader July 2006- Information about the volatility trend in stock market.

Banz,R.W; Relationship between Return and Market Value of Common Stocks, Journal of Finance, Vol.9 pp3-18.

Gogal K.R.(2005), Journal of finance,Feb.2006Various methods to analyse the Fund Performance pp23-25.

Jagric, T., Podobnik, B., and Kolanovic M., 2004, “Testing Efficiency Market Hypothesis for Six Transition Economies”, Bilten EDP, 3-4, 60-80.

Jensen, M., 1968, “The Performance of Mutual Fund in the period 1945-1964”, Journal of Finance, 23, 389-416.

Joshy Andrews, the management accountant, February 2007, pp 152.

Henning, C.N. and others, Financial Markets and the Economy, Prentice Hall, 1978, p.p. 145-149.

Mohantu P(jan.2007) Vikalpa-stock returns in India pp25-28

Sanjay Kant Khare, Southern Economist, January 15, 2007. pp21.

Sarma V.V.S.; Management Accountant; April, 2005, Vol. 40; No. 4; pp 274-275.

Azoff, E., Neural networks time series forecasting of financial markets,Wiley,1994

Back,A.,Weigend,A.,Afirstapplicationofindependentcomponentanalysisto extracting structure from stock returns. Int. J. on Neural Systems, 8(4):473– 484, 1998.

Becerra-Fernandez, I., Zanakis, S. Walczak,S., Knowledge discovery techniques for predicting country investment risk, Computers and Industrial Engineering Vol. 43 , Issue 4:787 – 800, 2002.

Berka, P. PKDD Discovery Challenge on Financial Data, In: Proceedings of the First International Workshop on Data Mining Lessons Learned, (DMLL2002), 8-12 July 2002, Sydney, Australia.

Bouchaud, J., Potters,M., Theory of Financial Risks: From Statistical Physics to Risk Management, 2000, Cambridge Univ. Press, Cambridge, UK.

Bratko, I., Muggleton, S., Applications of Inductive Logic Programming. Communications of ACM, 38(11): 65-70, 1995.

Kovalerchuk, B., Vityaev, E., Data Mining in Finance: Advances in Relational and Hybrid Methods, Kluwer, 2000.

Kovalerchuk, B., Vityaev E., Ruiz J.F., Consistent and Complete Data and "Expert Mining” in Medicine, In: Medical Data Mining and Knowledge Discovery, Springer, 2001, 238-280.

Trippi,R.,Turban,E.,Neuralnetworksinfinanceandinvesting,Irwin,Chicago, 1996.

Giles, G., Lawrence S., Tshoi, A. Rule inference for financial prediction using recurrent neural networks, In: Proc. Of IEEE/IAAFE Conference on Computational Intelligence for financial Engineering, IEEE, NJ, 1997, 253-259.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.