Details



A COMPREHENSIVE REVIEW OF MACHINE LEARNING TOOLS AND TECHNIQUES RELATING TO CUSTOMER PRODUCTS

Ruchika Chakravarti

108-116

Vol 7, Jan-Jun, 2018

Date of Submission: 2018-04-10 Date of Acceptance: 2018-05-16 Date of Publication: 2018-05-24

Abstract

Sentiment analysis is defined as the process of mining of data, view, review or sentence to Predict the emotion of the sentence through natural language processing (NLP) or Machine Learning Techniques. The significance of feeling analysis or review mining is growing daily as information develops. Machines should be dependable and productive in solving and figuring out human emotions and sentiments. Since clients offer their viewpoints and sentiments more transparently than any time in recent memory, feeling investigation is turning into a fundamental instrument to screen and figure out Sentiment. [2] Focuses on audit mining and opinion examination on the Amazon site.

References

  1. Callen Rain, “Sentiment Analysis in Amazon Reviews Using Probabilistic Machine Learning”, Swarthmore College Computer Society, November 2013
  2. P. Russom et al., “Big data analytics,” TDWI best practices report, fourth quarter, pp. 1–35, 2011.
  3. S. Erevelles, N. Fukawa, and L. Swayne, “Big data consumer analytics and the transformation of marketing,” Journal of Business Research, vol. 69, no. 2, 2016.
  4. V. Hatzivassiloglou and K. R. McKeown, “Predicting the semantic orientation of adjective s,” in Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 1997, pp. 174–181.
  5. T. Wilson, J. Wiebe, and P. Hoffmann, “Recognizing contextual polarity in phrase-level sentiment analysis,” in Proceedings of the conference on human language technology and empirical methods in natural language processing. Association for Computational Linguistics, 2005, pp. 347–354.
  6. B. Pang and L. Lee, “A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts,” in Proceedings of the 42nd annual meeting Association for Computational Linguistics. Association for Computational Linguistics, 2004, p. 271.
  7. A. Pak and P. Paroubek, “Twitter as a corpus for sentiment analysis and opinion mining.” in LREc, vol. 10, no. 2010, 2010.
  8. M. WAHYUDI and D. A. KRISTIYANTI, “Sentiment analysis of smartphone product review using support vector machine algorithm-based particle swarm optimization.” Journal of Theoretical & Applied Information Technology, vol. 91, no. 1, 2016.
  9. D. N. Devi, C. K. Kumar, and S. Prasad, “A feature based approach for sentiment analysis by using support vector machine,” in Advanced Computing (IACC), 2016 IEEE 6th International Conference on. IEEE, 2016, pp. 3–8.
  10. V. Narayanan, I. Arora, and A. Bhatia, “Fast and accurate sentiment classification using an enhanced naive bayes model,” in International Conference on Intelligent Data Engineering and Automated Learning. Springer , 2013, pp. 194–201.
  11. S. Tan and J. Zhang, “An empirical study of sentiment analysis for Chinese documents,” Expert Systems with applications, vol. 34, no. 4, pp. 2622–2629, 2008
  12. Samaneh Moghaddam and Martin Ester, “Opinion Digger: An Unsupervised Opinion Miner from Unstructured Product Reviews”, Proceedings of 19th ACM International Conference on Information and Knowledge Management, pp. 1825-1828, 2010.
  13. Vidisha M. Pradhan, Jay Vala, Prem Balani, “A Survey on Sentiment Analysis Algorithms for Opinion Mining”, International Journal of Computer Applications, Volume 133, No.9, January 2016
  14. G. Sneka, CT. Vidhya, “Algorithms for Opinion Mining and Sentiment Analysis: An Overview”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 6, Issue 2, February 2016
  15. Balahur, A., & Montoyo, A. (2008, October). A feature dependent method for opinion mining and classification. In Natural Language Processing and Knowledge Engineering, 2008. NLP-KE'08. International Conference on (pp. 1-7). IEEE.
  16. Asad, K. I., Ahmed, T., & Rahman, M. S. (2012, May). Movie popularity classification based on inherent movie attributes using C4. 5, PART and correlation coefficient. In Informatics, Electronics & Vision (ICIEV), 2012 International Conference on (pp. 747-752). IEEE.
  17. M. Hagenau, M. Liebmann, and D. Neumann, “Automated news reading: Stock price prediction based on financial news using context-capturing features,” Decision Support Systems, vol. 55, no. 3, pp. 685–697, 2013.
  18. T. Xu, Q. Peng, and Y. Cheng, “Identifying the semantic orientation of terms using s-hal for sentiment analysis,” Knowledge- Based Systems, vol. 35, pp. 27 9–289, 2012.
  19. I. Maks and P. Vossen, “A lexicon model for deep sentiment analysis and opinion mining applications,” Decision Support Systems, vol. 53, no. 4, pp. 68 0–688, 2012.
  20. G. Qiu, X. He, F. Zhang, Y. Shi, J. Bu, and C. Chen, “Dasa:dissatisfaction-oriented advertising based on sentiment analysis,” Expert Systems with Applications, vol. 37, no. 9, pp. 6182– 6191, 2010.
  21. T.-K. Fan and C.-H. Chang, “Blogger-centric contextual advertising,” Expert systems with applications, vol. 38, no. 3, pp.1777–1788, 2011
  22. 22. Preethi, G., Krishna, P. V, Obaidat, M. S., Saritha, V., & Yenduri, S. (2017). Application of Deep Learning to Sentiment Analysis for recommender system on cloud.
  23. Wang, Z., & Fey, A. M. (2018). Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery.
Download PDF
Back

alexistogel toto online

bandar alexistogel

alexistogel bandar gacor

alexistogel link

alexistogel online

alexistogel bandar togel

link alternatif alexistogel

alexistogel

alexistogel

alexistogel

alexistogel daftar

alexistogel toto macau

alexistogel bandar macau

alexistogel slot

alexistogel agen slot

situs alexistogel

alexistogel

alexistogel

alexistogel

alexistogel

alexistogel bandar slot

alexistogel

Alexistogel Toto Macau

bandar alexistogel

slot alexistogel

alexistogel bandar togel

alexistogel

alexistogel slot

alexistogel

daftar alexistogel

alexistogel online

rtp alexistogel

alexistogel slot

alexistogel gacor

link alternatif alexistogel

alexistogel login

alexistogel

alexistogel slot dana

agen togel online

bandar togel online

alexistogel rtp

alexistogel slot

alexistogel daftar

slot online dana

situs slot online

alexistogel

bandar togel online

slot online terpercaya

togel slot online

agen slot online gacor

rtp live slot online

bandar slot online

bandar slot online gacor

agen slot online

daftar bandar togel slot

bandar togel online

togel slot hari ini

link alternatif togel slot

rtp slot online gacor

slot online gacor

alexistogel terpercaya

rtp slot gacor

tips slot maxwin

togel slot gacor

prediksi togel

game slot gacor

trik slot online

prediksi togel jitu

daftar togel slot online

slot online gacor

trik slot bonus

prediksi togel

RTP LIVE

Bandar Toto Macau

Situs Slot Gacor

bandarbola855 resmi

bandarbola855 gacor

bandarbola855 slot

link bandarbola855

bandarbola855 rtp

bandarbola855 link

bandarbola855 bandar

bandarbola855

bandarbola855 slot

bandarbola855 terpercaya

bandarbola855 slot

bandarbola855 daftar

bandarbola855 link

bandarbola855

bandarbola855

bandarbola855

iosbet

iosbet

link iosbet

slot online iosbet

iosbet link login

slot iosbet

iosbet gacor

iosbet

slot iosbet

agen iosbet

bandar iosbet

iosbet

iosbet link

iosbet

iosbet

iosbet

iosbet

liatogel

login liatogel

liatogel totomacau