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Improved exponential cuckoo search method for sentiment analysis

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Abstract

Sentiment analysis is a type of contextual text mining that determines how people feel about emotional issues that are frequently discussed on social media. The sentiments of emotive data are analyzed using a variety of sentiment analysis approaches, including lexicon-based, machine learning-based, and hybrid methods. Unsupervised approaches, particularly clustering methods are preferred over other methods since they can be applied directly to unlabeled datasets. Therefore, a clustering method based on an improved exponential cuckoo search has been proposed in this study for sentiment analysis. The proposed clustering method finds the optimal cluster centers from emotive datasets, which are then utilized to determine the sentiment polarity of emotive contents. The proposed improved exponential cuckoo search is first tested on standard and CEC-2013 benchmark functions before being utilized to determine the best cluster centroids from sentimental datasets. To assess the efficiency of the proposed method, it has been compared with K-means, cuckoo search, grey wolf optimizer, grey wolf optimizer with simulated annealing, hybrid step size-based cuckoo search, and spiral cuckoo search on nine sentimental datasets. The Experimental results and statistical analysis have proven the efficacy of the proposed method.

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Data Availability

Data used in this research are publicly available for research purpose.

References

  1. Abed-Alguni BH, Paul DJ (2020) Hybridizing the cuckoo search algorithm with different mutation operators for numerical optimization problems. J Intell Syst 29(1):1043–1062

    Google Scholar 

  2. Agarwal P, Mehta S (2019) Subspace clustering of high dimensional data using differential evolution. In: Nature-inspired algorithms for big data frameworks. IGI Global, pp 47–74

  3. Agrawal R, Kaur B, Sharma S (2020) Quantum based whale optimization algorithm for wrapper feature selection. Appl Soft Comput 89:106092

    Google Scholar 

  4. Ahuja S, Dubey G (2017) Clustering and sentiment analysis on twitter data. In: 2017 2nd International conference on telecommunication and networks (TEL-NET). IEEE, pp 1–5

  5. Aljarah I, Mafarja M, Heidari AA, Faris H, Mirjalili S (2019) Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach. Knowl Inf Syst xx:1–33

    Google Scholar 

  6. Amiri E, Mahmoudi S (2016) Efficient protocol for data clustering by fuzzy cuckoo optimization algorithm. Appl Soft Comput 41:15–21

    Google Scholar 

  7. Bartolo N, Komatsu E, Matarrese S, Riotto A (2004) Non-gaussianity from inflation: theory and observations. Phys Rep 402:103–266

    MathSciNet  Google Scholar 

  8. Bezdek JC, Hathaway RJ (1994) Optimization of fuzzy clustering criteria using genetic algorithms. In: Proceeding of IEEE world congress on computational intelligence. USA, pp 589–594

  9. Blake C (1998) Uci repository of machine learning databases. https://archive.ics.uci.edu/ml/datasets.php. Accessed 24 July 2021

  10. Bondielli A, Marcelloni F (2019) A survey on fake news and rumour detection techniques. Inf Sci 497:38–55

    Google Scholar 

  11. Boonmee A, Sethanan K (2016) A glnpso for multi-level capacitated lot-sizing and scheduling problem in the poultry industry. Eur J Oper Res 250 (2):652–665

    MathSciNet  MATH  Google Scholar 

  12. Brest J, Bošković B, Zamuda A, Fister I, Mezura-Montes E (2013) Real parameter single objective optimization using self-adaptive differential evolution algorithm with more strategies. In: 2013 IEEE congress on evolutionary computation. IEEE, pp 377–383

  13. Canuto S, Gonçalves MA, Benevenuto F (2016) Exploiting new sentiment-based meta-level features for effective sentiment analysis. In: Proceeding of the ACM international conference on web search and data mining. USA, pp 53–62

  14. ChandraPandey A, SinghRajpoot D, Saraswat M (2018) Data clustering based on data transformation and hybrid step size-based cuckoo search. In: 2018 11th international conference on contemporary computing (IC3). IEEE, pp 1–6

  15. Chen T, Xu R, He Y, Wang X (2017) Improving sentiment analysis via sentence type classification using bilstm-crf and cnn. Expert Syst Appl 72:221–230

    Google Scholar 

  16. Chiong R, Fan Z, Hu Z, Adam MT, Lutz B, Neumann D (2018) A sentiment analysis-based machine learning approach for financial market prediction via news disclosures. In: Proceeding of the ACM genetic and evolutionary computation conference companion. Japan, pp 278–279

  17. Chourasia S, Sharma H, Singh M, Bansal JC (2019) Global and local neighborhood based particle swarm optimization. In: Harmony search and nature inspired optimization algorithms. Springer, pp 449–460

  18. Cobos C, Muñoz-Collazos H, Urbano-Muñoz R, Mendoza M, León E, Herrera-Viedma E (2014) Clustering of web search results based on the cuckoo search algorithm and balanced bayesian information criterion. Inf Sci 281:248–264

    Google Scholar 

  19. Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12:2493–2537

    MATH  Google Scholar 

  20. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18

    Google Scholar 

  21. Devi KN, Bhaskaran VM, Kumar GP (2015) Cuckoo optimized svm for stock market prediction. In: Proceeding of IEEE international conference on innovations in information, embedded and communication systems. India, pp 1–5

  22. El Alaoui I, Gahi Y, Messoussi R, Chaabi Y, Todoskoff A, Kobi A (2018) A novel adaptable approach for sentiment analysis on big social data. Journal of Big Data 5(1):1–18

    Google Scholar 

  23. El Ansari O, Zahir J, Mousannif H (2018) Context-based sentiment analysis: a survey. In: International conference on model and data engineering. Springer, pp 91–97

  24. Emary E, Zawbaa HM, Grosan C, Hassenian AE (2015) Feature subset selection approach by gray-wolf optimization. In: Afro-European conference for industrial advancement. Springer, pp 1–13

  25. Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381

    Google Scholar 

  26. Fernández-Gavilanes M, Álvarez-López T, Juncal-Martínez J, Costa-Montenegro E, González-Castaño FJ (2016) Unsupervised method for sentiment analysis in online texts. Expert Syst Appl 58:57–75

    Google Scholar 

  27. Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701

    MATH  Google Scholar 

  28. Gong Y, Shin K, Poellabauer C (2018) Improving liwc using soft word matching. In: Proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics, pp 523–523

  29. Hemmatian F, Sohrabi MK (2017) A survey on classification techniques for opinion mining and sentiment analysis. Artif Intell Rev:1–51

  30. Hu X, Tang J, Gao H, Liu H (2013) Unsupervised sentiment analysis with emotional signals. In: Proceeding of the ACM international conference on World Wide Web. Brazil, pp 607–618

  31. Hu X, Tang L, Tang J, Liu H (2013) Exploiting social relations for sentiment analysis in microblogging. In: Proceeding of the ACM international conference on web search and data mining. USAr, pp 537–546

  32. Hussain K, Mohd Salleh MN, Cheng S, Shi Y (2019) Metaheuristic research: a comprehensive survey. Artif Intell Rev 52(4):2191–2233

    Google Scholar 

  33. Janardana Naidu G, Seshashayee M (2021) Sentiment analysis for telugu text using cuckoo search algorithm. In: Smart computing techniques and applications. Springer, pp 253–257

  34. Katarya R, Verma OP (2018) Recommender system with grey wolf optimizer and fcm. Neural Comput Applic 30:1679–1687

    Google Scholar 

  35. Kumar V, Chhabra JK, Kumar D (2017) Grey wolf algorithm-based clustering technique. J Intell Syst 26:153–168

    Google Scholar 

  36. Kumar A, Jaiswal A, Garg S, Verma S, Kumar S (2019) Sentiment analysis using cuckoo search for optimized feature selection on kaggle tweets. International Journal of Information Retrieval Research (IJIRR) 9:1–15

    Google Scholar 

  37. Li J, Li Y-X, Tian S-S, Xia J-L (2020) An improved cuckoo search algorithm with self-adaptive knowledge learning. Neural Comput Applic 32(16):11967–11997

    Google Scholar 

  38. Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recognit 36(2):451–461

    Google Scholar 

  39. Loria S (2018) Textblob documentation. Release 0.15 2:269

    Google Scholar 

  40. Ma B, Yuan H, Wu Y (2017) Exploring performance of clustering methods on document sentiment analysis. J Inf Sci 43(1):54–74

    Google Scholar 

  41. Mandal S, Singh GK, Pal A (2021) Single document text summarization technique using optimal combination of cuckoo search algorithm, sentence scoring and sentiment score. Int J Inf Technol:1–9

  42. McHaney R, Tako A, Robinson S (2018) Using liwc to choose simulation approaches: a feasibility study. Decis Support Syst 111:1–12

    Google Scholar 

  43. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Google Scholar 

  44. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  45. Mirjalili SZ, Mirjalili S, Saremi S, Faris H, Aljarah I (2018) Grasshopper optimization algorithm for multi-objective optimization problems. Appl Intell 48(4):805–820

    Google Scholar 

  46. Mohammed AS, Shukla V, Pandey AC (2020) Enhancing sentiment analysis using enhanced whale optimisation algorithm. Int J Intell Inf Database Syst 13(2-4):208–230

    Google Scholar 

  47. Monish H, Pandey AC (2020) A comparative assessment of data mining algorithms to predict fraudulent firms. In: 2020 10th international conference on cloud computing data science & engineering (confluence). IEEE, pp 117–122

  48. Mukherjee A, Venkataraman V, Liu B, Glance NS (2013) What yelp fake review filter might be doing?. In: Proceeding of AAAI international conference on weblogs and social media. USA, pp 1–10

  49. Nagamma P, Pruthvi H, Nisha K, Shwetha N (2015) An improved sentiment analysis of online movie reviews based on clustering for box-office prediction. In: 2015 international conference in computing communication & automation (ICCCA). IEEE, pp 933–937

  50. Nasiri J, Khiyabani FM (2018) A whale optimization algorithm (woa) approach for clustering. Cogent Mathematics & Statistics:1483565

  51. Nawaz MS, Nawaz MZ, Hasan O, Fournier-Viger P, Sun M (2021) An evolutionary/heuristic-based proof searching framework for interactive theorem prover. Appl Soft Comput 104:107200

    Google Scholar 

  52. Norris P (2012) Political mobilization and social networks the example of the arab spring. Electron Democr 10:55–76

    Google Scholar 

  53. Ott M, Choi Y, Cardie C, Hancock JT (2011) Finding deceptive opinion spam by any stretch of the imagination. In: Proceeding of ACM conference on computational linguistics: human language technologies. USA, pp 309–319

  54. Pandey AC, Rajpoot DS (2020) Improving sentiment analysis using hybrid deep learning model. Recent Adv Comput Sci Commun (Formerly: Recent Patents on Computer Science) 13(4):627–640

    Google Scholar 

  55. Pandey AC, Rajpoot DS (2021) Feature selection method based on grey wolf optimization and simulated annealing. Recent Adv Comput Sci Commun (Formerly: Recent Patents on Computer Science) 14(2):635–646

    Google Scholar 

  56. Pandey AC, Tikkiwal VA (2021) Stance detection using improved whale optimization algorithm. Complex Intell Syst 7(3):1649–1672

    Google Scholar 

  57. Pandey AC, Rajpoot DS, Saraswat M (2016) Data clustering using hybrid improved cuckoo search method. In: 2016 9th international conference on contemporary computing (IC3). IEEE, pp 1–6

  58. Pandey AC, Rajpoot DS, Saraswat M (2017) Hybrid step size based cuckoo search. In: Proceeding of 10th IEEE international conference on contemporary computing (IC3). IEEE, pp 1–6

  59. Pandey AC, Rajpoot DS, Saraswat M (2017) Twitter sentiment analysis using hybrid cuckoo search method. Inf Process Manag 53(4):764–779

    Google Scholar 

  60. Pandey AC, Pal R, Kulhari A (2018) Unsupervised data classification using improved biogeography based optimization. Int J Syst Assur Eng Manag 9(4):821–829

    Google Scholar 

  61. Pandey AC, Garg M, Rajput S (2019) Enhancing text mining using deep learning models. In: 2019 12th International conference on contemporary computing (IC3). IEEE, pp 1–5

  62. Pandey AC, Tripathi AK, Pal R, Mittal H, Saraswat M (2019) Spiral salp swarm optimization algorithm. In: 2019 4th International conference on information systems and computer networks (ISCON). IEEE, pp 722–727

  63. Pandey AC, Rajpoot DS, Saraswat M (2020) Feature selection method based on hybrid data transformation and binary binomial cuckoo search. J Ambient Intell Humaniz Comput 11(2):719–738

    Google Scholar 

  64. Pandey AC, Kulhari A, Shukla DS (2021) Enhancing sentiment analysis using roulette wheel selection based cuckoo search clustering method. Journal of Ambient Intelligence and Humanized Computing:1–29

  65. Phu VN, Vo T (2018) K-medoids algorithm used for english sentiment classification in a distributed system. Comput Model New Technol 22(1):20–39

    Google Scholar 

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

    Google Scholar 

  67. Ray P, Chakrabarti A (2017) Twitter sentiment analysis for product review using lexicon method. In: Proceeding of IEEE international conference on data management, analytics and innovation. India, pp 211–216

  68. Riaz S, Fatima M, Kamran M, Nisar MW (2017) Opinion mining on large scale data using sentiment analysis and k-means clustering. Clust Comput:1–16

  69. Selim SZ, Alsultan K (1991) A simulated annealing algorithm for the clustering problem. Pattern Recognit 24:1003–1008

    MathSciNet  Google Scholar 

  70. Shen H, Jin L, Zhu Y, Zhu Z (2010) Hybridization of particle swarm optimization with the k-means algorithm for clustering analysis. In: Proceeding of IEEE international conference on bio-inspired computing: theories and applications. USA, pp 531–535

  71. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12:702–713

    Google Scholar 

  72. Sohangir S, Wang D, Pomeranets A, Khoshgoftaar TM (2018) Big data: deep learning for financial sentiment analysis. J Big Data 5:1–25

    Google Scholar 

  73. Strapparava C, valitutti A, Stock O (2006) The affective weight of lexicon. In: LREC, pp 423–426

  74. Sun H, Morales A, Yan X (2013) Synthetic review spamming and defense. In: Proceeding of IEEE international conference on knowledge discovery and data mining. USA, pp 1088–1096

  75. Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37:267–307

    Google Scholar 

  76. Testdata.manual.2009.06.14 (2021) http://help.sentiment140.com/for-students/. Accessed July 2021

  77. Tijare PV, Prathuri JR (2022) Correlation between k-means clustering and topic modeling methods on twitter datasets. In: Cyber security and digital forensics. Springer, pp 459–477

  78. Tripathi AK, Sharma K, Bala M (2018) A novel clustering method using enhanced grey wolf optimizer and mapreduce. Big Data Res 14:93–100

    Google Scholar 

  79. Twitter dataset (2021) http://twitter.com/download/iphone. Accessed July 2021

  80. Twitter-sanders-apple (2021) http://boston.lti.cs.cmu.edu/classes/95-865-K/HW/HW3/. Accessed July 2021

  81. Vashishtha S, Susan S (2021) Highlighting keyphrases using senti-scoring and fuzzy entropy for unsupervised sentiment analysis. Expert Syst Appl 169:114323

    Google Scholar 

  82. Wang H, Lu Y, Zhai C (2010) Latent aspect rating analysis on review text data: a rating regression approach. In: Proceeding of ACM international conference on Knowledge discovery and data mining. USA, pp 783–792

  83. Xia R, Xu F, Yu J, Qi Y, Cambria E (2016) Polarity shift detection, elimination and ensemble: a three-stage model for document-level sentiment analysis. Inf Process Manag 52:36–45

    Google Scholar 

  84. Xiong S, Ji D (2016) Exploiting flexible-constrained k-means clustering with word embedding for aspect-phrase grouping. Inf Sci 367:689–699

    Google Scholar 

  85. Xu D, Tian Y (2015) A comprehensive survey of clustering algorithms. Annals of Data Sci 2:165–193

    Google Scholar 

  86. Xue D, Wu L, Hong Z, Guo S, Gao L, Wu Z, Zhong X, Sun J (2018) Deep learning-based personality recognition from text posts of online social networks. Appl Intell:1–15

  87. Yang X-S (2014) Cuckoo search and firefly algorithm: overview and analysis. In: Cuckoo search and firefly algorithm. Springer, pp 1–26

  88. Yang X-S, Deb S (2009) Cuckoo search via lévy flights. In: Proceeding of IEEE world congress on nature & biologically inspired computing. India, pp 220–214

  89. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Proceeding of nature inspired cooperative strategies for optimization. Springer, UK

  90. Yang XS, Deb S (2014) Cuckoo search: recent advances and applications. Neural Comput Applic 24:169–174

    Google Scholar 

  91. Yue L, Chen W, Li X, Zuo W, Yin M (2018) A survey of sentiment analysis in social media. Knowl Inf Syst 5:1–47

    Google Scholar 

  92. Yusof NN, Mohamed A, Abdul-Rahman S (2015) Reviewing classification approaches in sentiment analysis. In: Proceeding of international conference on soft computing in data science. Springer, Singapore, pp 43–53

  93. Zainuddin N, Selamat A, Ibrahim R (2018) Hybrid sentiment classification on twitter aspect-based sentiment analysis. Appl Intell 48:1218–1232

    Google Scholar 

  94. Zainuddin N, Selamat A, Ibrahim R (2018) Hybrid sentiment classification on twitter aspect-based sentiment analysis. Appl Intell 48(5):1218–1232

    Google Scholar 

  95. Zaw MM, Mon EE (2013) Web document clustering using cuckoo search clustering algorithm based on levy flight. Int J Innov Appl Stud 4:182–188

    Google Scholar 

  96. Zhang Q, Couloigner I (2005) A new and efficient k-medoid algorithm for spatial clustering. In: International conference on computational science and its applications. Springer, pp 181–189

  97. Zhang Q, Liu W, Meng X, Yang B, Vasilakos AV (2017) Vector coevolving particle swarm optimization algorithm. Inf Sci 394:273–298

    Google Scholar 

  98. Zhang L, Wang S, Liu B (2018) Deep learning for sentiment analysis: a survey. Wiley Interdiscip Rev: Data Min Knowl Disc 8:1–25

    Google Scholar 

  99. Zhu J, Wang H, Mao J (2010) Sentiment classification using genetic algorithm and conditional random fields. In: Proceeding of IEEE international conference on information management and engineering. China, pp 193–96

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    Authors and Affiliations

    1. Discipline of Computer Science & Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India

      Avinash Chandra Pandey

    2. Government Polytechnic College, Bundi, Rajasthan, India

      Ankur Kulhari

    3. Department of Computer Science & Engineering, Indira Gandhi Delhi Technical University for Women, Delhi, India

      Himanshu Mittal

    4. Department of Computer Science, Malaviya National Institute of Technology, Jaipur, India

      Ashish Kumar Tripathi

    5. Department of Computer Science and Information Technology, Jaypee Institute of Information Technology, Noida, India

      Raju Pal

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    Pandey, A.C., Kulhari, A., Mittal, H. et al. Improved exponential cuckoo search method for sentiment analysis. Multimed Tools Appl 82, 23979–24029 (2023). https://doi.org/10.1007/s11042-022-14229-5

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