Cross Domain Sentiment Classification Using Natural Language Processing

Authors

  • S. Vidya  Assistant Professor, Department of Computer Science and Engineering, Kalasalingam Institute of Technology, Krishnankoil, Tamil Nadu, India

Keywords:

NLP, sentiment classification, domain thesaurus

Abstract

The field of study that focuses on the interactions between human language and computers is called Natural Language Processing. It sits at the intersection of computer science, artificial intelligence and Natural Language Processing.NLP can be a approach for computers to analyze, understand, and derive meaning from human language in an exceedingly good and helpful way. By utilizing NLP, developers can organize and structure information to perform tasks like automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.NLP systems have long crammed useful roles, like correcting descriptive linguistics, changing speech to text and automatically translating between languages.

References

  1. B. Pang and L. Lee, "Opinion mining and sentiment analysis,"Found. Trends Inf. Retrieval, vol. 2, nos. 1/2, pp. 1-135, 2008.
  2. Y. Lu, C. Zhai, and N. Sundaresan, "Rated aspect summarization of short comments," in Proc. 18th Int. Conf. World Wide Web, 2009,pp. 131-140.
  3. T.-K. Fan and C.-H. Chang, "Sentiment-oriented contextual advertising," Knowl. Inf. Syst., vol. 23, no. 3, pp. 321-344, 2010.
  4. M. Hu and B. Liu, "Mining and summarizing customer reviews," in Proc. 10th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2004, pp. 168-177.
  5. C. D. Manning and H. Sch€utze, Foundations of Statistical Natural Language Processing. Cambridge, MA, USA: MIT Press, 2002.
  6. H. Daume III, A. Kumar, and A. Saha, "Co-regularization based semi-supervised domain adaptation," in Proc. Adv. Neural Inf. Process. Syst. 23, 2010. pp. 478-486.
  7. D. Lopez-Paz, J. M. Hernandez-Lobato, and B. Scholkopf, "Semisupervised domain adaptation with non-parametric copulas," in Proc. Adv. Neural Inf. Process. Syst. 25, 2012, pp. 674-682.
  8. H. Daume III, "Frustratingly easy domain adaptation," in Proc.45th Annu. Meeting Assoc. Comput. Linguistics, 2007, pp. 256-263.
  9. J. Blitzer, R. McDonald, and F. Pereira, "Domain adaptation with structural correspondence learning," in Proc. Conf. Methods Natural Language Process., 2006, pp. 120-128.
  10. J. Blitzer, M. Dredze, and F. Pereira, "Biographies, bollywood,boom-boxes and blenders: Domain adaptation for sentiment classification

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-04-30

Issue

Volume 3, Issue 3, March-April-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
S. Vidya, " Cross Domain Sentiment Classification Using Natural Language Processing, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.348-353, March-April-2018.