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Recent Patents on Engineering

Editor-in-Chief

ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

Review Article

A Review on Sentiment Classification: Natural Language Understanding

Author(s): Srishty Jindal* and Kamlesh Sharma

Volume 13, Issue 1, 2019

Page: [20 - 27] Pages: 8

DOI: 10.2174/1872212112666180731113353

Price: $65

Abstract

Background: With the tremendous increase in the use of social networking sites for sharing the emotions, views, preferences etc. a huge volume of data and text is available on the internet, there comes the need for understanding the text and analysing the data to determine the exact intent behind the same for a greater good. This process of understanding the text and data involves loads of analytical methods, several phases and multiple techniques. Efficient use of these techniques is important for an effective and relevant understanding of the text/data. This analysis can in turn be very helpful in ecommerce for targeting audience, social media monitoring for anticipating the foul elements from society and take proactive actions to avoid unethical and illegal activities, business analytics, market positioning etc.

Method: The goal is to understand the basic steps involved in analysing the text data which can be helpful in determining sentiments behind them. This review provides detailed description of steps involved in sentiment analysis with the recent research done. Patents related to sentiment analysis and classification are reviewed to throw some light in the work done related to the field.

Results: Sentiment analysis determines the polarity behind the text data/review. This analysis helps in increasing the business revenue, e-health, or determining the behaviour of a person.

Conclusion: This study helps in understanding the basic steps involved in natural language understanding. At each step there are multiple techniques that can be applied on data. Different classifiers provide variable accuracy depending upon the data set and classification technique used.

Keywords: Sentiment analysis, feature selection, feature extraction, classification, language, behavior.

Graphical Abstract
[1]
G. Nandi, and A. Das, "A Survey on using data mining techniques feo online social network", IJCSI, vol. 10, pp. 162-167, 2013.
[2]
L.H. Patil, and M. Atique, "A novel feature selection based on information gain using wordnet", In Science and Information Conference,. London, UK, 2013, pp. 625-629
[3]
S. Poria, E. Cambria, A. Hussain, and G. Huang, "Towards an intelligent framework for multimodal affective data analysis", Neural Netw., vol. 63, pp. 104-116, 2015.
[4]
B. Liu, Sentiment Analysis and Opinion mining.. synthesis lectures on Human Language Technologies: Morgan & Claypool publishers, 2012.
[5]
B. Agarwal, S. Poria, N. Mittal, A. Gelbukh, and A. Hussain, "Concept - level sentiment analysis using dependency-based semantic parsing: A novel approach", Cognit. Comput., vol. 7, pp. 487-499, 2015.
[6]
B. Pang, L. Lee, and S. Vaithayanathan, "“Thumbs up? Sentiment Classification using machine learning techniques”, In", EMNLP '02 Proceedings Of The ACL-02 Conference On Empirical Methods in Natural Language Processing,. Stroudsburg, PA, USA, 2002, pp. 79-86,
[7]
Z. Hai, K. Chang, and J. Kim, "“Implicit feature identification via co-occurrence association rule mining”, In", Computational Linguistics and Intelligent Text Processing,. Tokyo, Japan, 2011, pp. 393- 404.
[8]
"N. Godbole, S. Skiena, and M. srinivasaiah,", “Large-scale sentiment analysis.”. U.S. Patent 7996210 B2, 2011.
[9]
M. Shardlow, “An analysis of feature selection techniques”,. The University of Manchester, Manchester, UK, 2016.
[10]
"I. guyon, and A. Ellisseeff, “An introduction to variable and feature selection", J. Mach. Learn. Res., vol. 3, pp. 1157-1182, 2003.
[11]
L. Zhang, R. Ghosh, and M.F. Dekhil, “Performing sentiment analysis”, U.S. Patent 9,009,024 B2, 2015.
[12]
"Pearson product-moment correlation coefficient: Available from:", http://en.wikipedia.org/wiki/Pearson_product_moment_correlation_coefficient
[13]
Y. Yang, and J.O. Pedersen, "“A comparative study on feature selection in text categorization”, In", Proceedings of the Fourteenth International Conference on Machine Learning,. Nashville, Tennessee, USA, 1997, pp. 412–420.
[14]
Y. Saeys, T. Abeel, and Y.V. de Peer, "“Robust feature selection using ensemble feature selection techniques”, In:", Machine Learning and Knowledge Discovery in Databases,. Lecture Notes in Computer Science, W. Daelemans, B. Goethals, and K. Morik, Ed. Berlin, Heidelberg: Springer, vol. 5212, 2008, pp. 313-325.
[15]
I. Perikos, and I. Hatzilygeroudis, "Recognizing emotions in text using ensemble of classifiers", Eng. Appl. Artif. Intell., vol. 51, pp. 191-201, 2016.
[16]
N.F. Da Silva, E.R. Hruschka, and E.R. Hruschka Jr, "Tweet sentiment analysis with classifier ensembles", Decis. Support Syst., vol. 66, pp. 170-179, 2014.
[17]
Q. Gu, Z. Li, and J. Han, "“Generalized fisher score for Feature selection”, In", UAI'11 Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence,. Barcelona, Spain 2011, pp. 266-273.
[18]
K. Arunasakthi, "L. KamatchiPriya, and A. Askerunisa, “Fisher score dimensionality reduction for svm classification", Int. J. Innov. Res. Sci. Eng., vol. 3, pp. 1900-1904, 2014.
[19]
Fisher Score Feature Selection Implementation: Available from:, https://stats.stackexchange.com/questions/277123/fisher-score-feature-selection-implementation
[20]
S. Lei, "“A feature selection method based on Information Gain and Genetic Algorithm”, In", International Conference on Computer Science and Electronics Engineering,. Hangzhou, China, 2012, pp. 355-358.
[21]
D. Koller, and M. Sahami, "“Toward optimal feature selection”, In", Proceedings of the Thirteenth International Conference on International Conference on Machine Learning,. Bari, Italy, 1996, pp. 284- 292.
[22]
"Dimensionality reduction: Available from:", http://people.cs. pitt.edu/~iyad/DR.pdf
[23]
M.S. Sainin, and R. Alfred, "“A genetic based wrapper feature selection approach using nearest neighbour distance matrix”, In", 3rd Conference on Data Mining and Optimization (DMO),. Putrajaya, Malaysia, 2011, pp. 237-242.
[24]
R. Kohavi, and G.H. John, "Wrappers for feature subset selection", Artif. Intell., vol. 97, pp. 273-324, 1997.
[25]
P.D. Turney, "“Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews”, In", Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics,. Philadelphia, PN, USA, 2002, pp. 417-424.
[26]
A. Pak, and P. Paroubek, "“Text representation using dependency tree sub-graphs for sentiment analysis”, In", DASFAA'11 Proceedings of the 16th International Conference on Database Systems for Advanced applications,. Hong Kong, China, 2011, pp. 323-332.
[27]
G. Sidorov, "Non-continuous syntactic sn-grams", Polibits, vol. 48, pp. 67-75, 2013.
[28]
S. Das, “Automatic document sentiment analysis”,. U.S. Patent 2017/0060996 A1, 2017.
[29]
I. Kononenko, "“Estimating attributes: Analysis and extensions of RELIEF”, In", European Conference on Machine Learning,. Catania, Italy, 1994, pp. 171-182.
[30]
E. Cambria, A. Hussain, C. Havasi, and C. Eckl, "“Common sense computing from society of mind to digital intuition and beyond”, In", European Workshop on Biometrics and Identity Management,. Madrid, Spain, 2009, pp. 252-259.
[31]
G.A. Miller, "WordNet: A lexical database for english", Commun. ACM, vol. 38, pp. 39-41, 1995.
[32]
C. Fellbaum, WordNet: An electronic lexical database.. Cambridge, MA: MIT Press, 1998.
[33]
T. Mikolov, K. Chen, G. Corrado, and J. Dean, "“Efficient estimation of word representations in vector space”,", CORR,. vol. abs/1301.3781, pp. 1-12, 2013.
[34]
B. Jiang, E. Xun, and J. Qi, "“A domain independent approach for extracting terms from research papers”, In", Databases Theory and Applications.. Lecture Notes in Computer Science, M. Sharaf, M. Cheema, and J. Qi, Ed. Cham: Springer, vol. 9093, 2015, pp. 155- 166
[35]
M.F. Delgado, E. Cernadas, and S. Barro, "Do we need hundreds of classifiers to solve real world classification problems", JMLR, vol. 15, pp. 3133-3181, 2014.
[36]
J.A. Rehling, and T.G. Dignan, “Detailed sentiment analysis”,. U.S. Patent 8,463,595 B1, 2013.
[37]
L. Brieman, "Random forests", Mach. Learn., vol. 45, pp. 5-32, 2001.
[38]
G. Biau, "Analysis of a random forest model", JMLR, vol. 13, pp. 1063-1095, 2012.
[39]
E. Goel, and E. Abhilasha, "Random forest: A review", IJARCSSE, vol. 7, pp. 251-257, 2017.
[40]
D. Zhang, H. Xu, Z. Su, and Y. Xu, "Chinese comments sentiment classification based on word2vec and SVMperf", ESA, vol. 2, pp. 1857-1863, 2015.
[41]
G. Patil, V. Galande, V. Kekan, and K. Dange, "Sentiment analysis using support vectormachine", IJIRCCE, vol. 2, pp. 2607-2612, 2014.
[42]
B. Savita, and D. Gore, "Sentiment Analysis on Twitter Data Using Support Vector Machine", IJCS, vol. 4, pp. 365-370, 2016.
[43]
P. Kalaivani, and K.L. Shunmuganathan, "Sentiment classification of movie reviews by supervised machine learning approaches", IJCSE, vol. 4, pp. 285-292, 2013.
[44]
A. Alsaffar, and N. Omar, "Integrating a lexicon based approach and k nearest neighbour for malay sentiment analysis", JCS, vol. 11, pp. 639-644, 2015.
[45]
J. Kazmierska, and J. Malicki, "Application of the naïve Bayesian classifier to optimize treatment decisions", Radiother. Oncol., vol. 86, pp. 211-216, 2008.
[46]
L. Dey, S. Chakraborty, A. Biswas, B. Bose, and S. Tiwari, "Sentiment analysis of review datasets using Naïve Bayes’ and K-NN classifier", IJIEEB, vol. 4, pp. 54-62, 2016.
[47]
Preety and S. Dahiya, "Sentiment analysis using SVM and Naïve Bayes algorithm", IJCSMC, vol. 4, pp. 212-219, 2015.
[48]
H.Y. Lee, and H. Renganathan, "“Chinese sentiment analysis using maximum entropy”, In", Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology,. Chiang Mai, Thailand, 2011, pp. 89-93.
[49]
N. Mehra, S. Khandelwal, and P. Patel, “Sentiment Identification using maximum entropy analysis of movie reviews”,. Stanford University, USA, 2002.
[50]
W. Medhat, A. Hassan, and H. Korashy, "Sentiment analysis algorithms and applications: A survey", ASEJ, vol. 5, pp. 1093-1113, 2014.
[51]
K. Ravi, and V. Ravi, "A survey on opinion mining and sentiment analysis: tasks, approaches and applications", KBS, vol. 89, pp. 14-46, 2015.

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