Aspect sentiment identification using random Fourier features

Автор: Thara S., Athul Krishna N. S.

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 9 vol.10, 2018 года.

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The objective of the paper was to show the effectiveness of using random Fourier features in detection of sentiment polarities. The method presented in this paper proves that detection of aspect based polarities can be improved by selective choice of relevant features and mapping them to lower dimensions. In this study, random Fourier features were prepared corresponding to the polarity data. A regularized least square strategy was adopted to fit a model and perform the task of polarity detection Experiments were performed with 10 cross-validations. The proposed method with random Fourier features yielded 90% accuracy over conventional classifiers. Precision, Recall, and F-measure were deployed in our empirical evaluations.

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Aspect Based Sentiment, Kernel, Least Square Regression, Random Fourier Feature, Sentiment

Короткий адрес: https://readera.ru/15016524

IDR: 15016524   |   DOI: 10.5815/ijisa.2018.09.04

Список литературы Aspect sentiment identification using random Fourier features

  • J. Wang. Encyclopedia of Data Warehousing and Mining, 2nd edn., vol 2. , Idea Group Inc., Hershey, 2008
  • R.Quirk, S.Greenbaum, G. Leech and J. Svartvik. A Comprehensive Grammar of the English Language. 1st edn. vol 2., Longman Publication Company: London, 1985.
  • Minqing Hu and Bing Liu.. 2004. “Mining and summarizing customer reviews,” in Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD,04, pages 168–177.
  • Dusti Hillard, Mari Ostendorf, and Elizabeth Shriberg 2003. “Detection of agreement vs. disagreement in meetings: Training with unlabeled data,” in Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Volume 2, NAACL ’03, pages 34–36.
  • Roberto Gonzalez-Ibanez, Smaranda Muresan and Nina Wacholder (2011). “Identifying sarcasm in Twitter: a Closer Look,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistic, ACLHLT ’11, pages 581–586.
  • Christine Liebrecht, Florian Kunneman, and Antal Van den Bosch,” The perfect solution for detecting sarcasm in tweets,” in Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 29–37.
  • M. Kaya, G. Fidan, and I. H. Toroslu, “Sentiment analysis of Turkish political news, “in Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology-. IEEE Computer Society, Volume 01,pp. 174–180
  • Noriaki Kawamae, “Hierarchical Approach to Sentiment Analysis,” 2012 IEEE Sixth International Conference on Semantic Computing. May, pp.140-146.
  • Lopes Rosa, R., Zegarra Rodríguez, D., & Bressan, G. (2013). “SentiMeter-Br: A Social Web Analysis Tool to Discover Consumers' Sentiment,” in Mobile Data Management (MDM), IEEE 14th International Conference, 2013, Vol. 2, pp. 122-124.
  • J. Chen, Y. Liu, G. Zhang, Y. Cai, T. Wang and H. Min, "Sentiment Analysis for Cantonese Opinion Mining," Emerging Intelligent Data and Web Technologies (EIDWT), Fourth International Conference on, Xi'an, 2013, pp. 496-500.
  • W. Duan, Q. Cao, Y. Yu, “Mining online user generated content: Using sentiment analysis technique to study hotel service quality,” in Proceedings 46th Hawaii International Conference on System Sciences, pp. 3119-3128.
  • V.Hatzivassiloglou and K.R. McKeown, “Predicting the semantic orientation of adjectives,” in Proceedings of the 35th Annual Meeting of the ACL and the 8th Conference of the European Chapter of the ACL (1997), pp 174–181.
  • A. Esuli and F. Sebastiani, “Determining term subjectivity and term orientation for opinion mining,” in Proceedings of the European Chapter of the Association for Computational Linguistics, 2006, pp. 193–200.
  • P. D. Turney and M. L. Littman, “Measuring praise and criticism: Inference of semantic orientation from association,” ACM Transactions on Information Systems (TOIS), 2003, vol. 21, pp. 315–346.
  • Bo P, Lee L, Vaithyanathan S, “Thumbs up? Sentiment classification using machine learning techniques” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP); 2002. p. 79-86.
  • Kreutzer, J., Witte N.: Opinion Mining using SentiWordNet. Uppsala University (2013).
  • Wiebe J., Mihalcea R.: “Word sense and subjectivity,” in Proceedings of COLING/ACL-06, 2006, Pages 1065-1072.
  • Arun, S., Anand Kumar, M., Soman, K. P, “Sentiment Analysis of Tamil movie reviews via Feature Frequency Count,” IJAER, 2015, pp.17934-17939.
  • Deepu, S. N., Jisha, P. J., Rajeev, RR, Elizabeth, S.: SentiMa-Sentiment Extraction for Malayalam, ICACCI, 2014, pp. 1719-1723.
  • Sandeep, C., Bhadran, V.K., Santhosh, G., Manoj, K. P.: “Document level Sentiment Extraction for Malayalam Feature based Domain Independent Approach,” IJARTET, 2015.
  • Stanford Named Entity Recognizer (NER). Available: https://nlp.stanford.edu/software/CRF-NER.shtml. Date accessed: 07/03/2017
  • Stanford Log-linear Part-Of-Speech Tagger. Available: http://nlp.stanford.edu/software/tagger.shtml. Date accessed: 07/03/2017
  • A. Rahimi and B. Recht, “Random features for large-scale kernel machines, “in Proc. 21st Ann. Conf. Advances in Neural Information Processing Systems (NIPS), 2007, pp. 1-8.
  • S. Sachin Kumar, B. Premjith, M. Anand Kumar, K.P.Soman:AMRITA_CEN-NLP@SAIL2015: “Sentiment Analysis in Indian Language Using Regularized Least Square Approach with Randomized Feature Learning,” MIKE 2015, pp. 671-683.
  • Aspect Based Sentiment Analysis. Available: http://alt.qcri.org/semeval2015/task12/. Date accessed: 07/02/2017
  • R. Rifkin, G. Yeo, and T. Poggio, “Regularized least-squares classification,” Nato Science Series Sub Series III Computer and Systems Sciences, 2003, pp. 131–154.
  • Andrea Tacchetti and Pavan K. Mallapragada and Matteo Santoro and Lorenzo Rosasco, “GURLS: A Least Squares Library for Supervised Learning,” Journal of Machine Learning Research, 2013, vol 14, pp. 3201-3205.
  • W. Rudin. Fourier Analysis on Groups. Wiley Classics Library. Reprint of the 1962 edition A Wiley-Interscience Publication, New York, 1994
  • Weka 3: Data Mining Software in Java. Available: http://www.cs.waikato.ac.nz/ml/weka/. Date accessed: 14/04/2017
  • Jotheeswaran J, Koteeswaran S. “Feature Selection using Random Forest Method for Sentiment Analysis,” Indian Journal of Science and Technology, 2016 Jan, 9(3), pp. 1-7.
  • Sasikala, S.; Bharathidason, S.; Jothi Venkateswaran, C., “Improving Classification Accuracy Based On Random Forest Model Through Weighted Sampling For Noisy Data With Linear Decision Boundary,” Indian Journal Of Science And Technology, April 2015, pp. 614-619
  • Thara.S, Sidharth S, “SVD feature based Aspect Sentiment Classication” ICACCI, 2017, pp.2370-2374.
  • Ibrahim S. I. Abuhaiba, Hassan M. Dawoud,"Combining Different Approaches to Improve Arabic Text Documents Classification", International Journal of Intelligent Systems and Applications (IJISA), Vol.9, No.4, pp.39-52, 2017. DOI: 10.5815/ijisa.2017.04.05
  • Ayman E. Khedr, S.E.Salama, Nagwa Yaseen,"Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.7, pp.22-30, 2017. DOI: 10.5815/ijisa.2017.07.03
  • Sudhir Kumar Sharma, Ximi Hoque,"Sentiment Predictions using Support Vector Machines for Odd-Even Formula in Delhi", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.7, pp.61-69, 2017. DOI: 10.5815/ijisa.2017.07.07
  • MATLAB. Available :https://in.mathworks.com/ products/matlab.html
  • JavaProgramming.Available: https://www.javapoint.com/java-programs
  • NetBeans. Available: http://netbeans.org/downloads/
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