We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Survey on Sentiment Analysis

by Preeti Routray, Chinmaya Kumar Swain, Smita Praya Mishra
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 76 - Number 10
Year of Publication: 2013
Authors: Preeti Routray, Chinmaya Kumar Swain, Smita Praya Mishra
10.5120/13280-0527

Preeti Routray, Chinmaya Kumar Swain, Smita Praya Mishra . A Survey on Sentiment Analysis. International Journal of Computer Applications. 76, 10 ( August 2013), 1-8. DOI=10.5120/13280-0527

@article{ 10.5120/13280-0527,
author = { Preeti Routray, Chinmaya Kumar Swain, Smita Praya Mishra },
title = { A Survey on Sentiment Analysis },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 76 },
number = { 10 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume76/number10/13280-0527/ },
doi = { 10.5120/13280-0527 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:45:31.020024+05:30
%A Preeti Routray
%A Chinmaya Kumar Swain
%A Smita Praya Mishra
%T A Survey on Sentiment Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 76
%N 10
%P 1-8
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The explosive growth of the textual information on the web in the past few decades has brought radical change in human life. In the web, people share their opinions and views (sentiments) in many forms about products or services they are aware of. This creates a large collection of opinions and views in the form of texts, which needs to be analysed to know the efficacy of the product or service. Opinions are usually subjective expressions that describe person's sentiment, feelings towards the object or service. The sentiment can be positive or negative. This survey is a summary of the work on sentiment analysis, covering the new challenges which appear in sentiment analysis as compared to traditional fact based analysis. Currently there are four research challenges for sentiment analysis. Those are subjectivity classification, word sentiment classification, document sentiment classification and opinion extraction. This survey discusses related issues of sentiment analysis and main approaches to those problems.

References
  1. Boiy, E. Hens, P. , Deschacht, K. &Moens, M. F. , "Automatic Sentiment Analysis in Online Text," In Proceedings of the Conference on Electronic Publishing(ELPUB-2007), pp. 349-360 , 2007.
  2. Liu, B. , "Sentiment analysis and subjectivity," In Handbook of Natural Language Processing, Second Edition, N. Indurkhya and F. J. Damerau, Eds. CRC Press, Taylor and Francis Group, Boca Raton, FL. ISBN 978-1420085921, 2010.
  3. Kim, S. -M. &Hovy, E. , "Determining the Sentiment of Opinions,". In Proceedings of the International Conference on Computational Linguistics (COLING 2004), pp. 1367-1373, 2004.
  4. Hiroshi, K. , Tetsuya, N. & Hideo, W. , "Deeper Sentiment Analysis Using Machine Translation Technology," In Proceedings of the 20th International Conference on Computational Linguistics, pp. 494-500, 2004.
  5. Morinaga, S. , Yamanishi, K. , Tateishi, K. , & Fukushima, T. , "Mining product reputations on the web", In Proceeding of ACM SIGKDD 2002, pp. 341 - 349, 2002.
  6. Banea, C. , Mihalcea, R. , Wiebe, J. & Hassan, S. , "Multilingual Subjectivity Analysis Using Machine Translation" In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pp. 127-135, 2008.
  7. Taboada, M. , Gillies, M. A. , &McFetridge, P. , "Sentiment classification techniques for tracking literary reputation", In Proceedings of LREC 2006, pp. 36 - 43, 2006.
  8. Dave, K. , Lawrence, S. &Pennock, D. M. , "Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews", In Proceedings of the 12th International Conference on World Wide Web, pp. 519-528, 2003.
  9. Hu, M. &Lui, B. , "Mining and Summarizing Customer Reviews". In Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 168 - 177, 2004.
  10. Pang, B. & Lee, L. , "A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts", In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, pp. 271 - 278, 2004.
  11. Pang, B. , Lee, L. &Vaithyanathan, S. , "Thumbs Up? Sentiment Classification Using Ma-chine Learning Techniques", In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 79 - 86,2002.
  12. Pang, B. & Lee, L. , "Opinion Mining and Sentiment Analysis", Foundations and Trends in Information Retrieval,Vol. 2 (1-2), pp. 1 - 135 , 2008.
  13. Esuli, A. &Sebastiani, F. , "Determining Term Subjectivity and Term Orientation for Opinion Mining", In Proceedings the 11th Meeting of the European Chapter of the Association for Computational Linguistics, pp. 193 - 200, 2006.
  14. Alm, C. O. , Roth, D. &Sproat, R. , "Emotions from Text: Machine Learning for Text-Based Emotion Prediction", In Proceedings of the Human Language Technologies Conference/ Conference on Empirical Methods in Natural Language Processing, pp. 347 - 354, 2005.
  15. Wiebe, J. &Mithalcea, R. , "Word sense and subjectivity", In Proceedings of COLING-ACL, pp. 1065 - 1072, 2006.
  16. Lloyd, L. , Kechagias, D. &Skiena, S. , "Lydia: A System for Large-Scale News Analysis", In String Processing and Information Retrieval, pp. 161-166, 2005.
  17. Balog, K. , Mishne, G. & De Rijke, M. , "Why Are They Excited? Identifying and Explaining Spikes in Blog Mood Levels", In Proceedings of the 11th Meeting of the European Chapter of the Association for Computational Linguistics, pp. 207-210 , 2006.
  18. Yu, H. &Hatzivassiloglou, V. , "Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences", In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 129 - 136, 2003.
  19. SwapnaSomasundaran and JanyceWiebe,"Recognizing Stances in Online Debates", in Proceedings of the Association for Computational Linguist (ACL), 2008.
  20. Mishne, G. , & de Rijke, M. , "Capturing global mood levels using blog posts", In AAAI-2006 spring symposium on computational approaches to analysing weblogs, 2006.
  21. Hatzivassiloglou, V. , &McKeown, K. R. , "Predicting the semantic orientation of adjectives", In Proceedings of the 35th annual meeting of ACL, 1997.
  22. Esuli, A. , &Sebastiani, F. , "Determining the semantic orientation of terms through gloss classication", In Proceedings of CIKM-05, the ACM SIGIR conference on information and knowledge management, Bremen, DE, 2005.
  23. Esuli, A. , &Sebastiani, F. , "SentiWordNet: A publicly available lexical resource for opinion mining", In Proceedings of LREC, 2006.
  24. Andreevskaia, A. , &Bergler, S. , "Mining WordNet for a fuzzy sentiment: Sentiment tag extraction from WordNet glosses", In Proceedings EACL-06, Trento,Italy, 2006.
  25. Kamps, J. , Marx, M. , Mokken, R. J. , & de Rijke, M. ,"Using WordNet to measure semantic orientation of adjectives", In Proceedings of 4th international conference on language resources and evaluation, Lisbon, PT (Vol. IV, pp. 1115 - 1118), 2004.
  26. Domingos, P. , Pazzani, M. J. , "On the optimality of the simple bayesian classifier under zero-one loss", Machine Learning, 29(2-3), pp. 103 - 130, 1997.
  27. McCallum, A. , and Nigam, K. , "A comparison of event models for Naive Bayes text classification", In AAAI-98 workshop on learning for text categorization, pp. 41 - 48, 1998.
  28. Joachims, T. , "Text categorization with support vector machines: Learning with many relevant features", In Proceedings of 10th European Conference on Machine Learning (ECML-98), pp. 137 - 142, 1998.
  29. Carpenter, B. , "Scaling high-order character language models to gigabytes", In Proceedings of the 2005 association for computational linguistics software workshop, pp. 1 - 14, 2005.
  30. Adam L. Berger, Stephen A. Della Pietra, and Vincent J. Della Pietra, "A maximum entropy approach to natural language processing", Computational Linguistics, 22(1): 39 - 71, 1996.
  31. Kamal Nigam, John La erty, and Andrew McCallum, "Using maximum entropy for text classification", in Proceedings of the IJCAI-99, pp. 61 - 67, 1999.
  32. Zhu Jian, Xu Chen & Han-shi, "Sentiment classification using the theory of ANNs", The Journal of China Universities of Posts and Telecommunications, 17(Suppl. ): 58 - 62, 2010.
  33. Songbo Tan and Yuefen Wang, "Weighted SCL model for adaptation of sentiment classification", Expert Systems with Applications, 38, pp. 10524 - 10531, 2011.
  34. Ando, R. , & Zhang, T. , "A framework for learning predictive structures from multiple tasks and unlabeled data", JMLR, 6, pp. 1817 - 1853, 2005.
  35. Blitzer, J. , Dredze, M. , &Pereir, F. , "Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification", in Proceedings of 45th annual meeting of ACL, 2007.
  36. Blitzer, J. , McDonald, R. , & Pereira, F. , "Domain adaptation with structural correspondence learning", Proceedings of EMNLP, 2006.
  37. Chelba, C. , &Acero, A. , "Adaptation of maximum entropy capitalizer: Little data can help a lot", in Proceedings of EMNLP, 2004.
  38. Dai, W. , Xue, G. , Yang, Q. , & Yu, Y. , "Transferring Naive Bayes classifiers for text classification", In Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI 2007), July 22-26, 2007.
  39. Daume, H. III, &Marcu, D. , "Domain adaptation for statistical classifiers", Journal of Artificial Intelligence Research, 26, pp. 101-126.
  40. Du, W. ,Tan, S. ,Cheng, X. & Yun, X. , "Adapting information bottleneck method for automatic construction of domain-oriented sentiment lexicon", in Proceedings of WSDM, 2010.
  41. Jing Jiang and ChengXiangZhai, "A two-stage approach to domain adaptation for statistical classfiers", In Proceedings of the ACM 16th Conference on Information and Knowledge Management (CIKM'07), pages 401-410, 2007.
  42. Xiao Li and Je Bilmes, "A Bayesian Divergence Prior for Classifier Adaptation", in Proceedings of Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-2007) , San Juan, Puerto Rico, March 2007.
  43. Chuong Do and Andrew Y. Ng, "Transfer learning for text classification", Advances in Neural Information Processing Systems, Volume: 18, Issue: 3, Pages: 299 - 306, 2006.
  44. Songbo Tan, Gaowei Wu, Huifeng Tang, Xueqi Cheng, "A novel scheme for domain-transfer problem in the context of sentiment analysis, in Proceedings of CIKM 2007,pp. 979 - 982.
  45. Songbo Tan, Xueqi Cheng, Yuefen Wang, HongboXu, "Adapting Naive Bayes to Domain Adaptation for Sentiment Analysis", in Proceedings of ECIR, pp. 337 - 349, 2009.
  46. Yuanbin Wu, Qi Zhang, Xuanjing Huang, and Lide Wu. , "Phrase dependency parsing for opinion mining", In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 1533 - 1541,USA, 2009.
  47. Bartlett, jake, and Albright, Russ, "Coming to a TheaterNear You! Sentiment Classification Techniques Using SAS Text Miner", in Proceeding of SAS Global Forum, 2008.
  48. M. RushdiSaleh, M. T. Martn-Valdivia, A. Montejo-Rez, L. A. Urea-Lpez, "Experiments with SVM to classify opinions in different domains", Expert Systems with Applications, pp. 14799 - 14804, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Sentiment Analysis Machine Learning Sentiment Classification WordNet Support Vector Machine Naive Bayes Maximum Entropy Language Model

Powered by PhDFocusTM