Top

The Journal of Supercomputing

Published in:

12-11-2020

An application of MOGW optimization for feature selection in text classification

Authors: Razieh Asgarnezhad, S. Amirhassan Monadjemi, Mohammadreza Soltanaghaei

Published in: The Journal of Supercomputing | Issue 6/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Due to extensive web applications, sentiment classification (SC) has become a relevant issue of interest among text mining experts. The extensive online reviews prevent the application of effective models to be used in companies and in the decision making of individuals. Pre-processing greatly contributes in sentiment classification. The traditional bag-of-words approaches do not record multiple relationships among words. In this study, emphasis is on the pre-processing stage and data reduction techniques, which would make a big difference in sentiment classification efficiency. To classify opinions, a multi-objective-grey wolf-optimization algorithm is proposed where the two objectives aim for decreasing the error of Naïve Bayes and K-nearest neighbour classifiers and a neural network as the final classifier. In evaluating this proposed framework, three datasets are applied. By obtaining 95.76% precision, 95.75% accuracy, 95.99% recall, and 95.82% f-measure, it is evident that this framework outperforms its counterparts.

previous article Design and implementation of multiplication algorithm in quantum-dot cellular automata with energy dissipation analysis

next article A dynamic VM consolidation approach based on load balancing using Pearson correlation in cloud computing

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Gao H, Zeng X, Yao C (2019) Application of improved distributed naive Bayesian algorithms in text classification. J Supercomput 75(9):5831–5847CrossRef

Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retriev. https://doi.org/10.1561/1500000001CrossRef

Pang B, Lee L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, Barcelona, Spain, pp 271–278. https://doi.org/10.3115/1218955.1218990

Abbas A, Hussein QM (2020) Twitter Sentiment Analysis Using an Ensemble Majority Vote Classifier. J Southwest Jiaotong Univ. https://doi.org/10.35741/issn.0258-2724.55.1.9CrossRef

Ahmad S, Zakwan M, Syafira N, Moziyana N (2019) A review of feature selection and sentiment analysis technique in issues of Propaganda. Int J Adv Comput Sci Appl. https://doi.org/10.14569/IJACSA.2019.0101132CrossRef

Alsaeedi A, Khan MZ (2019) A study on sentiment analysis techniques of twitter data. Int J Adv Comput Sci Appl. https://doi.org/10.14569/IJACSA.2019.0100248CrossRef

Verma B, Thakur RS (2018) Sentiment analysis using lexicon and machine learning-based approaches: a survey. In: Proceedings of International Conference on Recent Advancement on Computer and Communication, Lecture Notes in Networks and Systems, Springer, Singapore. https://doi.org/10.1007/978-981-10-8198-9_46

Zhang H, Gan W, Jiang B (2014) Machine learning and lexicon based methods for sentiment classification: a survey. In: Proceeding of the 11th Web Information System and Application Conference, IEEE, Tianjin, China. https://doi.org/10.1109/WISA.2014.55

Abdulla NA, Ahmed NA, Shehab MA, Al-Ayyoub M, Al-Kabi MN, Al-rifai S (2014) Towards improving the lexicon-based approach for arabic sentiment analysis. Int J Inf Technol Web Eng 9(3):55–71CrossRef

10.

Nawaz A, Asghar S, Naqvi SHA (2019) A segregational approach for determining aspect sentiments in social media analysis. J Supercomput 75(5):2584–2602CrossRef

11.

Alnawas A, Arici N (2018) The corpus based approach to sentiment analysis in modern standard Arabic and Arabic dialects: a literature review. Politeknik Dergisi 21(2):461–470

12.

Cruz L, Ochoa J, Roche M, Poncelet P (2017) Dictionary-based sentiment analysis applied to a specific domain. In: Proceeding of the 3rd. Annual Internacional Symposium on Information Management and Big Data, Communications in Computer and Information Science, Springer, Cham. https://doi.org/10.1007/978-3-319-55209-5_5

13.

Phu VN, Chau VTN, Tran VTN, Dat ND (2018) A Vietnamese adjective emotion dictionary based on exploitation of Vietnamese language characteristics. Artif Intell Rev 50:93–159. https://doi.org/10.1007/s10462-017-9538-6CrossRef

14.

Kumar CSP, Babu LDD (2020) Evolving dictionary based sentiment scoring framework for patient authored text. Evol Intel. https://doi.org/10.1007/s12065-020-00366-zCrossRef

15.

Park S, Kim Y (2016) Building thesaurus lexicon using dictionary-based approach for sentiment classification. In: Proceeding of the 14th International Conference on Software Engineering Research, Management and Applications, IEEE, Towson, MD, USA. https://doi.org/10.1109/SERA.2016.7516126

16.

Kumar A, Khorwal R (2017) Firefly algorithm for feature selection in sentiment analysis. In: Computational Intelligence in Data Mining. Singapore, Springer. pp 693–703. https://doi.org/10.1007/978-981-10-3874-7_66

17.

Shang L, Zhou Z, Liu X (2016) Particle swarm optimization-based feature selection in sentiment classification. Soft Comput 20(10):3821–3834CrossRef

18.

Manek AS, Shenoy PD, Mohan MC et al (2017) Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World wide web 20(2):135–154. https://doi.org/10.1007/s11280-015-0381-xCrossRef

19.

Zhuang L, Jing F, Zhu X-Y (2006) Movie review mining and summarization. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management. ACM, pp 43–50. https://doi.org/10.1145/1183614.1183625

20.

Severyn A, Moschitti A, Uryupina O et al (2016) Multi-lingual opinion mining on YouTube. Inf Process Manag 52(1):46–60CrossRef

21.

Poria S, Cambria E, Gelbukh A (2016) Aspect extraction for opinion mining with a deep convolutional neural network. Knowl-Based Syst 108:42–49CrossRef

22.

Chen L, Qi L (2011) Social opinion mining for supporting buyers’ complex decision making: exploratory user study and algorithm comparison. Social Netw Anal Min 1(4):301–320. https://doi.org/10.1007/s13278-011-0023-yCrossRef

23.

Chaovalit P, Zhou L (2005) Movie review mining: a comparison between supervised and unsupervised classification approaches. In: Proceedings of the 38th Annual Hawaii International Conference on System Sciences. IEEE, Big Island, HI, USA, pp 1–9. https://doi.org/10.1109/HICSS.2005.445

24.

Dave K, Lawrence S, Pennock DM (2003) Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th International Conference on World Wide Web. ACM, 2003, pp 519–528. https://doi.org/10.1145/775152.775226

25.

Kumar A, Jaiswal A (2019) Swarm intelligence based optimal feature selection for enhanced predictive sentiment accuracy on twitter. Multimed Tools Appl 78(20):29529–29553. https://doi.org/10.1007/s11042-019-7278-0CrossRef

26.

Rashaideh H, Sawaie A, Al-Betar MA et al (2018) A grey wolf optimizer for text document clustering. J Intell Syst 29(1):814–830. https://doi.org/10.1515/jisys-2018-0194CrossRef

27.

Movie review data set. https://www.cs.cornell.edu/people/pabo/movie-review-data/

28.

Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, pp 168–177. https://doi.org/10.1145/1014052.1014073

29.

Rosenthal S, Farra N, Nakov P (2017) SemEval-2017 task 4: sentiment analysis in Twitter. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), Vancouver, Canada, pp 502–518. https://doi.org/10.18653/v1/S17-2088

30.

Nakov P, Ritter A, Rosenthal S et al (2019) SemEval-2016 task 4: Sentiment analysis in Twitter, In: 10th International Workshop on Semantic Evaluation (SemEval-2016), Association for Computational Linguistics, San Diego, California, pp 1–18. https://doi.org/10.18653/v1/S16-1001

31.

Deshmukh JS, Tripathy AK (2018) Entropy based classifier for cross-domain opinion mining. Appl Comput Inform 14(1):55–64. https://doi.org/10.1016/j.aci.2017.03.001CrossRef

32.

Lin C, He Y (2009) Joint sentiment/topic model for sentiment analysis. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management. ACM, pp 375–384. https://doi.org/10.1145/1645953.1646003

33.

Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, CambridgeCrossRef

34.

Nguyen DQ, Nguyen Dat Q, Vu T et al (2014) Sentiment classification on polarity reviews: an empirical study using rating-based features. In: Proceeding if the 5th Workshop on Computational Approaches to Subjectivity. Sentiment and Social Media Analysis, Baltimore, Maryland, pp 128–135. https://doi.org/10.3115/v1/W14-2621

35.

Cha S-H (2007) Comprehensive survey on distance/similarity measures between probability density functions. Int J Math Models Methods Appl Sci 4(1):300–307

36.

Alpaydin E (2014) Introduction to machine learning. MIT press, CambridgeMATH

37.

Han J, Micheline K, Jian P (2012) Data mining: concepts and techniques. Morgan Kaufmann Elsevier, Burlington. https://doi.org/10.1016/C2009-0-61819-5CrossRefMATH

38.

Coello CAC, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems. Springer, Berlin. https://doi.org/10.1007/978-0-387-36797-2CrossRefMATH

39.

Zitzler E (1999) Evolutionary algorithms for multiobjective optimization: methods and applications. Citeseer. https://doi.org/10.1.1.39.9023

40.

Korosec P (2010) New achievements in evolutionary computation. BoD–books on demand. https://doi.org/10.5772/3083

41.

Deb K, Pratap A, Agarwal S et al (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197. https://doi.org/10.1109/4235.996017CrossRef

42.

Deeply moving: deep learning for sentiment analysis. https://nlp.stanford.edu/sentiment/

Title: An application of MOGW optimization for feature selection in text classification
Authors: Razieh Asgarnezhad
S. Amirhassan Monadjemi
Mohammadreza Soltanaghaei
Publication date: 12-11-2020
Publisher: Springer US
Published in: The Journal of Supercomputing / Issue 6/2021
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-020-03490-w

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 6/2021

Performance and power consumption analysis of Arm Scalable Vector Extension

An evolutionary game approach to IoT task offloading in fog-cloud computing

Forecasting peak energy demand for smart buildings

Classification of hyperspectral remote sensing image via rotation-invariant local binary pattern-based weighted generalized closest neighbor

Performance comparison of multi-container deployment schemes for HPC workloads: an empirical study

OKCM: improving parallel task scheduling in high-performance computing systems using online learning

Premium Partner