A Survey Paper on Sentiment Analysis : Approches, Methods & Challenges

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2019 by IJCTT Journal
Volume-67 Issue-10
Year of Publication : 2019
Authors : Dr.K.Anuradha, Dr.M.Vamsi Krishna, Dr.Banitamani Mallik, Prof.B.P.Mishra
  10.14445/22312803/IJCTT-V67I10P105

MLA

MLA Style:Dr.K.Anuradha, Dr.M.Vamsi Krishna, Dr.Banitamani Mallik,Prof.B.P.Mishra. Festus  "A Survey Paper on Sentiment Analysis : Approches, Methods & Challenges," International Journal of Computer Trends and Technology 67.10 (2019):25-34.

APA Style Dr.K.Anuradha, Dr.M.Vamsi Krishna, Dr.Banitamani Mallik,Prof.B.P.Mishra.A Survey Paper on Sentiment Analysis : Approches, Methods & Challenges International Journal of Computer Trends and Technology, 67(10),25-34.

Abstract
Sentiment Analysis is the domain of understanding these emotions with software, and it’s a must-understand for developers and business leaders in a modern workplace. As with many other fields, advances in Deep Learning have brought Sentiment Analysis into the foreground of cutting-edge algorithms. Today we use natural language processing, statistics, and text analysis to extract, and identify the sentiment of text into positive, negative, or neutral categories. In this paper, an attempt is made to give an overview of different methods available for sentiment analysis, along with different approaches, challenges in sentiment analysis

Reference
[1] B. Pang, et al., "Thumbs up?: sentiment classification using machine learning techniques," in Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, 2002, pp. 79-86.
[2] Turney, Peter D. "Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews." Proceedings of the 40th annual meeting on association for computational linguistics.Association for Computational Linguistics, 2002.
[3] Rajini Singh, RajdeepKaur, Sentiment analysis on social media and online review.
[4] http://www.ebizmba.com/articles/social-networking-websites
[5] S.-M. Kim and E. Hovy, "Determining the sentiment of opinions," in Proceedings of the 20th international conference on Computational Linguistics, 2004, p. 1367.
[6] T. Wilson, et al., "Recognizing contextual polarity in phrase-level sentiment analysis," in Proceedings of the conference on human language technology and empirical methods in natural language processing, 2005, pp. 347-354.
[7] ShahidShayaa, et al, “Sentiment analysis of Big Data: Methods Applications and Challenges”, IEEE Access ,Vol – 6 Issue 2018, Pg: 37807 – 37827
[8] F. R. Lucini et al., "Text mining approach to predict hospital admissions using early medical records from the emergency department", Int. J. Med. Inf., vol. 100, pp. 1-8, Apr. 2017.
[9] Z. Khan, T. Vorley, "Big data text analytics: An enabler of knowledge management", J. Knowl. Manage., vol. 21, no. 1, pp. 18-34, 2017.
[10] T. T. Thet, J.-C. Na, C. S. G. Khoo, "Aspect-based sentiment analysis of movie reviews on discussion boards", J. Inf. Sci., vol. 36, no. 6, pp. 823-848, 2010.
[11] H. Yu,V.Hatzivassiloglou, "Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences", Proc. Conf. Empirical Methods Natural Lang. Process., pp. 129-136, 2003.
[12] R. Piryani, D. Madhavi, V. K. Singh, "Analytical mapping of opinion mining and sentiment analysis research during 2000–2015", Inf. Process. Manage., vol. 53, no. 1, pp. 122-150, 2017.
[13] A. Qazi, A. Tamjidyamcholo, R. G. Raj, G. Hardaker, C. Standing, "Assessing consumers’ satisfaction and expectations through online opinions: Expectation and disconfirmation approach", Comput. Hum. Behav., vol. 75, pp. 450-460, Oct. 2017.
[14] N. Jindal, B. Liu, "Identifying comparative sentences in text documents", Proc. 29th Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retr., pp. 244-251, 2006.
[15] A. Qazi, R. G. Raj, M. Tahir, E. Cambria, K. B. S. Syed, "Enhancing business intelligence by means of suggestive reviews", Sci. World J., vol. 2014, Jun. 2014.
[16] M. Quigley, Encyclopedia of Information Ethics and Security, Hershey, PA, USA:IGI Global, 2007.
[17] Cambria, "Affective computing and sentiment analysis", IEEE Intell.Syst., vol. 31, no. 2, pp. 102-107, Mar./Apr. 2016.
[18] S. Poria, E. Cambria, N. Howard, G.-B. Huang, A. Hussain, "Fusing audio visual and textual clues for sentiment analysis from multimodal content", Neurocomputing, vol. 174, pp. 50-59, Jan. 2016.
[19] S. Poria, I. Chaturvedi, E. Cambria, A. Hussain, "Convolutional MKL based multimodal emotion recognition and sentiment analysis", Proc. IEEE 16th Int. Conf. Data Mining (ICDM), pp. 439-448, Dec. 2016.
[20] S. Poria, E. Cambria, A. Gelbukh, "Aspect extraction for opinion mining with a deep convolutional neural network", Knowl.-Based Syst., vol. 108, pp. 42-49, Sep. 2016.
[21] Chaturvedi, E. Cambria, S. Poria, R. Bajpai, "Bayesian deep convolution belief networks for subjectivity detection", Proc. IEEE 16th Int. Conf. Data Mining Workshops (ICDMW), pp. 916-923, Dec. 2016.
[22] S. Poria, E. Cambria, D. Hazarika, P. Vij, A deeper look into sarcastic tweets using deep convolutional neural networks, 2016, [online] Available: https://arxiv.org/abs/1610.08815.
[23] Cambria, B. White, "Jumping NLP curves: A review of natural language processing research", IEEE Comput. Intell. Mag., vol. 9, no. 2, pp. 48-57, May 2014.
[24] A. Zaslavsky, C. Perera, D. Georgakopoulos, Sensing as a service and big data, 2013, [online] Available: https://arxiv.org/abs/1301.0159.
[25] D. Denyer, D. Tranfield, "Producing a systematic review" in The Sage Handbook of Organizational Research Methods, Thousand Oaks, CA, USA:Sage, 2009.
[26] D. Tranfield, D. Denyer, P. Smart, "Towards a methodology for developing evidence-informed management knowledge by means of systematic review", Brit. J. Manage., vol. 14, no. 3, pp. 207-222, 2003.
[27] R. J. Light, D. B. Pillemer, Summing Up: The Science of Reviewing Research, Cambridge, MA, USA:Harvard Univ. Press, 1984.
[28] D. M. Rousseau, J. Manning, D. Denyer, "11 evidence in management and organizational science: Assembling the field’s full weight of scientific knowledge through syntheses", Acad. Manage. Ann., vol. 2, no. 1, pp. 475-515, 2008.
[29] S. L. Newbert, "Empirical research on the resource-based view of the firm: An assessment and suggestions for future research", Strategic Manage. J., vol. 28, no. 2, pp. 121-146, 2007.
[30] Cambria, B. Schuller, Y. Xia, C. Havasi, "New avenues in opinion mining and sentiment analysis", IEEE Intell. Syst., vol. 28, no. 2, pp. 15-21, Mar. 2013.
[31] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, M.Stede, "Lexicon-based methods for sentiment analysis", Comput.Linguistics, vol. 37, no. 2, pp. 267-307, 2011.
[32] A. Giachanou, F. Crestani, "Like it or not: A survey of Twitter sentiment analysis methods", ACM Comput. Surv., vol. 49, no. 2, pp. 28, 2016.
[33] C. C. Aggarwal, C. Zhai, "A survey of text classification algorithms" in Mining Text Data, Boston, MA, USA:Springer, pp. 163-222, 2012.
[34] P. Domingos, "A few useful things to know about machine learning", Commun.ACM, vol. 55, no. 10, pp. 78-87, 2012.
[35] I. H. Witten, E. Frank, M. A. Hall, C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, San Mateo, CA, USA:Morgan Kaufmann, 2016.
[36] D. H. Wolpert, W. G. Macready, "No free lunch theorems for search", 1995.
[37] W. S. McCulloch, W. Pitts, "A logical calculus of the ideas immanent in nervous activity", Bull. Math. Biophys., vol. 5, no. 4, pp. 115-133, 1943.
[38] C. A. L. Bailer-Jones, R. Gupta, H. P. Singh, "An introduction to artificial neural networks" in Automated Data Analysis in Astronomy, New Delhi, India:Narosa Publishing House, vol. 52, pp. 18, Sep. 2001.
[39] E. Grossi, M. Buscema, "Introduction to artificial neural networks", Eur. J. Gastroenterol.Hepatol., vol. 19, pp. 1046-1054, Dec. 2007.
[40] A.-L. Boulesteix, S. Janitza, J. Kruppa, I. König, "Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics", Wiley Interdiscipl. Rev. Data Mining Knowl.Discovery, vol. 2, no. 6, pp. 493-507, 2012.
[41] A. Liaw, M. Wiener, "Classification and regression by randomforest", R News, vol. 2, no. 3, pp. 18-22, 2002.
[42] R. E. Schapire, Y. Freund, P. Bartlett, W. S. Lee, "Boosting the margin: A new explanation for the effectiveness of voting methods", Ann. Stat., vol. 26, no. 5, pp. 1651-1686, 1998.
[43] L. Breiman, "Bagging predictors", Mach. Learn., vol. 24, no. 2, pp. 123-140, 1996.
[44] T. Joachims, "Text categorization with support vector machines: Learning with many relevant features", Proc. Eur. Conf. Mach. Learn., vol. 98, pp. 137-142, 1998.
[45] C.-W. Hsu, C.-J. Lin, "A comparison of methods for multiclass support vector machines", IEEE Trans. Neural Netw., vol. 13, no. 2, pp. 415-425, Mar. 2002.
[46] S. N. Sivanandam, S. N. Deepa, "Genetic algorithms" in Introduction to Genetic Algorithms, New York, NY, USA:Springer-Verlag, 2007.
[47] M. Mitchell, "An introduction to genetic algorithms", Sadhana, vol. 24, no. 4, pp. 293-315, Aug. 1999.
[48] D. E. Goldberg, Genetic Algorithms in Search Optimization and Machine Learning, Reading, MA, USA:Addison-Wesley, 1989.
[49] A. Kumar, R. M. Pathak, Y. P. Gupta, "Genetic algorithm based approach for file allocation on distributed systems", Comput. Oper. Res., vol. 22, no. 1, pp. 41-54, 1995.
[50] D. D. Lewis, "Naive (Bayes) at forty: The independence assumption in information retrieval", Proc. Eur. Conf. Mach. Learn., pp. 4-15, 1998.
[51] M. Sahami, S. Dumais, D. Heckerman, E. Horvitz, "A Bayesian approach to filtering junk e-mail", Proc. Learn. Text Categorization Papers Workshop, vol. 62, pp. 98-105, 1998.
[52] J. R. Quinlan, "Induction of decision trees", Mach. Learn., vol. 1, no. 1, pp. 81-106, 1986.

Keywords
Sentiment Analysis, Data Mining, SVM, Deep Learning Algorithms