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Neighbour adjusted dispersive flies optimization based deep hybrid sentiment analysis framework

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Abstract

A very crucial branch of Natural Language Processing is Sentiment Analysis, which seeks to elicit feelings in the public from feedback provided by users. This study proposes a Hybridized Deep Neural Network-based framework for the sentiment analysis, where we have modified Dispersive Flies Optimization by adjusting its neighbor counterpart and applied that Neighbour Adjusted Dispersive Flies optimization for optimizing feature space with the aid of sentiment information extracted using our specially developed SentiWordNet lexicon-linked fitness function, after preliminary processing of data. This modification helps to avoid the local optimal solution and supports the optimization process to approach the global optimal solution in more effective way. Next, to handle the textual features efficiently through Deep Learning approaches, we use pre-trained embedding technique to represent them mathematically. The Hybridized Deep Neural Network, which is made up of a Convolutional Neural Network and Long Short Term Memory, is then given the embedded features. In order to store locally implanted information, Convolutional Neural Networks construct hierarchical representations, while Long Short Term Memory attempts to recollect pertinent prior data for opinion categorization. This hybridization helps to take advantage of both the component networks. The deep neural network system ultimately delivers the desired sentiment category. To demonstrate its effectiveness, the suggested hybrid methodology is reckoned and contrasted with numerous cutting-edge methodologies utilizing a variety of performance indicators. Our proposed framework gives the best performance compared to the baselines with an accuracy of 89.0%, 81.9%, 67.9%, 64.6%, 83.2%, 79.8% and 91.3% for Amazon, ETSY, Big Basket, Facebook, Finance, Twitter and Wine dataset respectively.

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Data availability statement

In this work, we have collected the datasets from [55,56,57,58,59,60] and [61] which is discussed in Section 4.1.

References

  1. Deng ZH, Luo KH, Yu HL (2014) A study of supervised term weighting scheme for sentiment analysis. Expert Syst Appl 41(7):3506–3513

    Article  Google Scholar 

  2. Collomb A, Costea C, Joyeux D, Hasan O, Brunie L (2014) A study and comparison of sentiment analysis methods for reputation evaluation. Rapport De recherche RR-LIRIS-2014-002

  3. Boiy E, Moens MF (2009) A machine learning approach to sentiment analysis in multilingual web texts. Inf Retr 12(5):526–558

    Article  Google Scholar 

  4. Yang CS, Shih HP (2012) A rule-based approach for effective sentiment analysis. In: PACIS, p 181

  5. Ding X, Liu B, Yu PS (2008) A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 international conference on web search and data mining, pp 231–240

  6. Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307

    Article  Google Scholar 

  7. Dey RK, Das AK (2023) Modified term frequency-inverse document frequency based deep hybrid framework for sentiment analysis. Multimed Tools Appl:1–24

  8. Solanki A, Bamrara R, Kumar K, Singh N (2020) VEDL: a novel video event searching technique using deep learning. In: Soft computing: theories and applications. Springer, pp 905–914

  9. Yasmin G, Das AK, Nayak J, Vimal S, Dutta S (2022) A rough set theory and deep learning-based predictive system for gender recognition using audio speech. Soft Comput:1–24

  10. Sakshi Kukreja V (2023) Image segmentation techniques: statistical, comprehensive, semi-automated analysis and an application perspective analysis of mathematical expressions. Arch Comput Methods Eng 30(1):457–495

    Article  Google Scholar 

  11. Kukreja V et al (2021) A retrospective study on handwritten mathematical symbols and expressions: classification and recognition. Eng Appl Artif Intell 103:104292

    Article  Google Scholar 

  12. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25

  13. Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. arXiv:1404.2188

  14. Jianqiang Z, Xiaolin G, Xuejun Z (2018) Deep convolution neural networks for twitter sentiment analysis. IEEE Access 6:23253–23260

    Article  Google Scholar 

  15. Chen, G (2016) A gentle tutorial of recurrent neural network with error backpropagation. arXiv:1610.02583

  16. Sundermeyer M, Ney H, Schlüter R (2015) From feedforward to recurrent LSTM neural networks for language modeling. IEEE/ACM Trans Audio, Speech, Lang Process 23(3):517–529

    Article  Google Scholar 

  17. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  18. Bodapati JD, Veeranjaneyulu N, Shareef SN (2019) Sentiment analysis from movie reviews using LSTMS. Ingenierie des Systemes d’Information 24(1)

  19. Ghosh A, Das S, Mallipeddi R, Das AK, Dash SS (2017) A modified differential evolution with distance-based selection for continuous optimization in presence of noise. IEEE Access 5:26944–26964

    Article  Google Scholar 

  20. Al-Rifaie MM (2014) Dispersive flies optimisation. In: 2014 Federated conference on computer science and information systems. IEEE, pp 529–538

  21. Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J 5(4):1093–1113

    Article  Google Scholar 

  22. Sebastiani F, Esuli A (2006) SentiwordNet: a publicly available lexical resource for opinion mining. LREC 6:417–422

    Google Scholar 

  23. Wordnet|a lexical database for english. https://wordnet.princeton.edu/. Accessed 15 Sep 2023

  24. Dang Y, Zhang Y, Chen H (2009) A lexicon-enhanced method for sentiment classification: an experiment on online product reviews. IEEE Intell Syst 25(4):46–53

    Article  Google Scholar 

  25. Kang IS (2013) A comparative study on using sentiwordnet for english twitter sentiment analysis. J Korean Inst Intell Syst 23(4):317–324

    Google Scholar 

  26. Kukreja V et al (2023) Recent trends in mathematical expressions recognition: an lda-based analysis. Expert Syst Appl 213:119028

    Article  Google Scholar 

  27. Zhao J, Zeng D, Xiao Y, Che L, Wang M (2020) User personality prediction based on topic preference and sentiment analysis using LSTM model. Pattern Recognit Lett 138:397–402

    Article  Google Scholar 

  28. Tripathi M (2021) Sentiment analysis of nepali Covid19 tweets using NB SVM and LSTM. J Artif Intell 3(03):151–168

    Google Scholar 

  29. Allahverdipour A, Soleimanian Gharehchopogh F (2018) An improved k-nearest neighbor with crow search algorithm for feature selection in text documents classification. J Adv Comput Res 9(2):37–48

    Google Scholar 

  30. AL-Deen MS, Yu L, Aldhubri A, Qaid GR (2022) Study on sentiment classification strategies based on the fuzzy logic with crow search algorithm. Soft Comput 26(22):12611–12622

  31. Onan A, Korukoglu S, Bulut H (2016) A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification. Expert Syst Appl 62:1–16

    Article  Google Scholar 

  32. Dixit A, Mani A, Bansal R (2020) DEPSOSVM: variant of differential evolution based on pso for image and text data classification. Int J Intell Comput Cybern 13(2):223–238

    Article  Google Scholar 

  33. Al-Rifaie MM, Aber A (2016) Dispersive flies optimisation and medical imaging. In: Recent advances in computational optimization: results of the workshop on computational optimization WCO 2014. Springer, pp 183–203

  34. Behera M, Sarangi A, Mishra D, Mallick PK, Shafi J, Srinivasu PN, Ijaz MF (2022) Automatic data clustering by hybrid enhanced firefly and particle swarm optimization algorithms. Mathematics 10(19):3532

    Article  Google Scholar 

  35. Kumar A, Khorwal R (2017) Firefly algorithm for feature selection in sentiment analysis. In: Computational intelligence in data mining: proceedings of the international conference on CIDM. Springer, pp 693–703

  36. Swapnarekha H, Dash PB, Pelusi D (2023) An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets. Math Biosci Eng 20(2):2382–2407

    Article  Google Scholar 

  37. Iqbal F, Hashmi JM, Fung BC, Batool R, Khattak AM, Aleem S, Hung PC (2019) A hybrid framework for sentiment analysis using genetic algorithm based feature reduction. IEEE Access 7:14637–14652

    Article  Google Scholar 

  38. Govindarajan M (2013) Sentiment analysis of movie reviews using hybrid method of naive bayes and genetic algorithm. Int J Adv Comput Res 3(4):139

    Google Scholar 

  39. Mosa MA (2020) A novel hybrid particle swarm optimization and gravitational search algorithm for multi-objective optimization of text mining. Appl Soft Comput 90:106189

    Article  Google Scholar 

  40. Goel L, Garg A (2018) Sentiment analysis of social networking websites using gravitational search optimization algorithm. Int J Appl Evol Comput (IJAEC) 9(1):76–85

    Article  Google Scholar 

  41. Behera MP, Sarangi A, Mishra D, Sarangi SK (2023) A hybrid machine learning algorithm for heart and liver disease prediction using modified particle swarm optimization with support vector machine. Procedia Comput Sci 218:818–827

    Article  Google Scholar 

  42. Behera MP, Sarangi A, Mishra D (2021) Analysis of Gaussian and Cauchy mutations in k-means particle swarm optimization algorithm for data clustering. Tech Adv Mach Learn Healthc:103–117

  43. Kumar Gupta D, Srikanth Reddy K, Shweta, Ekbal A (2015) PSO-asent: feature selection using particle swarm optimization for aspect based sentiment analysis. In: International conference on applications of natural language to information systems. Springer, pp 220–233

  44. Badr EM, Salam MA, Ali M, Ahmed H (2019) Social media sentiment analysis using machine learning and optimization techniques. Int J Comput Appl 975:8887

    Google Scholar 

  45. Wu C, Khishe M, Mohammadi M, Taher Karim SH, Rashid TA (2023) Evolving deep convolutional neutral network by hybrid sine-cosine and extreme learning machine for real-time covid19 diagnosis from x-ray images. Soft Comput 27(6):3307–3326

    Article  Google Scholar 

  46. Talaei Pashiri R, Rostami Y, Mahrami M (2020) Spam detection through feature selection using artificial neural network and sine-cosine algorithm. Math Sci 14:193–199

    Article  MathSciNet  Google Scholar 

  47. Internet slang dictionary & text slang translator. https://www.noslang.com/. Accessed 15 Sep 2023

  48. Complete list of text abbreviations & acronyms | webopedia. https://www.webopedia.com/reference/text-message-abbreviations/. Accessed 15 Sep 2023

  49. Dey RK, Das AK (2022) A simple strategy for handling ‘NOT’ can improve the performance of sentiment analysis. In: Computational intelligence in pattern recognition: proceedings of CIPR 2022. Springer, pp 255–267

  50. nLP-replace apostrophe/short words in python-stack overflow. https://stackoverflow.com/questions/43018030/replace-apostrophe-short-words-in-python. Accessed 15 Sep 2023

  51. Zhang Y, Jin R, Zhou ZH (2010) Understanding bag-of-words model: a statistical framework. Int J Mach Learn Cybern 1(1–4):43–52

    Article  Google Scholar 

  52. Introduction to word embedding and word2vec|by dhruvil karani|towards data science. https://towardsdatascience.com/introduction-to-word-embedding-and-word2vec-652d0c2060fa. Accessed 15 Sep 2023

  53. Enríquez F, Troyano JA, López-Solaz T (2016) An approach to the use of word embeddings in an opinion classification task. Expert Syst Appl 66:1–6

    Article  Google Scholar 

  54. Github-mmihaltz/word2vec-googlenews-vectors: word2vec google news model. https://github.com/mmihaltz/word2vec-GoogleNews-vectors. Accessed 15 Sep 2023

  55. Abdulelah. Etsy reviews|kaggle. https://www.kaggle.com/csabdulelah/etsy-seller-reviews. Accessed 15 Sep 2023

  56. Siddhartha M. Amazon alexa reviews | kaggle. https://www.kaggle.com/sid321axn/amazon-alexa-reviews. Accessed 15 Sep 2023

  57. Wolber L. Facebook_reviews_trustpilot | kaggle. https://www.kaggle.com/leonwolber/facebook-reviews-trustpilot. Accessed 15 Sep 2023

  58. Varshney A. "Big basket" google play app reviews for basic nlp | kaggle. https://www.kaggle.com/apurvavarshney/big-basket-google-play-app-reviews-for-basic-nlp. Accessed 15 Sep 2023

  59. Agrawal D. Tweetsentimentanalysis/twitter.csv at master \(\cdot \) dakshitagrawal/tweetsentimentanalysis \(\cdot \) github. https://github.com/dakshitagrawal/TweetSentimentAnalysis/blob/master/Twitter.csv. Accessed 15 Sep 2023

  60. Sinha A. Sentiment analysis for financial news | kaggle. https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news. Accessed 15 Sep 2023

  61. Rai R. Wine reviews | kaggle. https://www.kaggle.com/krrai77/wine-reviews. Accessed 15 Sep 2023

  62. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  Google Scholar 

  63. Cohen’s kappa - wikipedia. https://en.wikipedia.org/wiki/Cohen_kappa. Accessed 15 Sep 2023

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Acknowledgements

In this work we acknowledge University Grants Commission(UGC) of India for providing assistantship in terms of fellowship.

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  1. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, 711103, Howrah, West Bengal, India

    Ranit Kumar Dey & Asit Kumar Das

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  1. Ranit Kumar Dey
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Correspondence to Ranit Kumar Dey.

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Dey, R.K., Das, A.K. Neighbour adjusted dispersive flies optimization based deep hybrid sentiment analysis framework. Multimed Tools Appl 83, 64393–64416 (2024). https://doi.org/10.1007/s11042-023-17953-8

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