Skip to main content

Advertisement

Log in

Sentiment classification of movie reviews using GA and NeuroGA

  • 1199: Computational Intelligence Revolution in Multimedia Data Analytics and Business Management
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Views or comments expressed in favor or against of any item, a product or a movie, etc. are often available in the form of sentiments of users. These reviews are analyzed with an aim to provide meaningful information to the provider of the product and help in guiding the future users in a more meaningful way. In this manuscript, two different machine learning algorithms are considered for classification of movie reviews. Firstly, Genetic Algorithm (GA), where the movie reviews under analysis are transformed into chromosomes and these chromosomes are then classified using proper technique. Secondly, a combination of GA and Artificial Neural Network (ANN) is considered for the classification purpose. The best fit chromosomes obtained from GA is considered as input for ANN and further processing is carried out by changing the hidden nodes in ANN. The performance of these classifiers are then evaluated using different parameters like recall, precision, f-measure and accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Abbasi A, Chen H, Salem A (2008) Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums. ACM Trans Inf Syst (TOIS) 26(3):12

    Article  Google Scholar 

  2. AlBadani B, Shi R, Dong J (2022) A novel machine learning approach for sentiment analysis on twitter incorporating the universal language model fine-tuning and SVM. Appl Syst Innov, 5(1)

  3. Aue A, Gamon M (2005) Customizing sentiment classifiers to new domains: A case study. Proceedings of Recent Advances in Natural Language Processing (RANLP), Borovets, Bulgaria 1(3):1–7

    Google Scholar 

  4. Babatunde O, Armstrong L, Leng J, Diepeveen D (2014) A genetic algorithm-based feature selection. British Journal of Mathematics & Computer Science 4(21):889–905

    Google Scholar 

  5. Balage Filho PP, Avanċo L., Pardo TA, Nunes MG (2014) Nilc usp: An improved hybrid system for sentiment analysis in twitter messages. SemEval 2014:428

    Google Scholar 

  6. Beasley D, Martin RR, Bull DR (1993) Rath, An overview of genetic algorithms: Part 1. Fundamentals, University Computing 15:58–68

    Google Scholar 

  7. Dadhich A, Thankachan B (2022) Sentiment analysis of amazon product reviews using hybrid rule-based approach. In: Smart systems: innovations in computing. Springer, Singapore, pp 173–193

  8. Das A, Bandyopadhyay S (2010) Subjectivity detection using genetic algorithm, 1st Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA10), Lisbon, Portugal

  9. Ekman P, Friesen WV (1971) Constants across cultures in the face and emotion. In: Journal of personality and social psychology, 17(2), American Psychological Association, pp 124

  10. Fei Hao, Ren Yafeng, Shengqiong W u, Li Bobo, Ji Donghong (2021) Latent target-opinion as prior for document-level sentiment classification: A variational approach from fine-grained perspective. Inproceedings of the web conference 2021:553–564

    Google Scholar 

  11. Feldman R (2013) Techniques and applications for sentiment analysis. Commun ACM 56(4):82–89

    Article  Google Scholar 

  12. Garreta R, Moncecchi G (2013) Learning scikit-learn: Machine Learning in Python, Packt Publishing Ltd

  13. Gautam G, Yadav D (2014) Sentiment analysis of twitter data using machine learning approaches and semantic analysis. In: 2014 Seventh international conference on contemporary computing (IC3), IEEE, pp 437–442

  14. Govindarajan M (2013) Sentiment analysis of movie reviews using hybrid method of naive bayes and genetic algorithm. International Journal of Advanced Computer Research 3(4):139

    Google Scholar 

  15. Hady MFA, Schwenker F (2013) Semi-supervised learning. In: Handbook on neural information processing, Springer, pp 215–239

  16. Hastie T, Tibshirani R, Friedman J (2009) Unsupervised learning. Springer

  17. Holland JH (1975) Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. U Michigan Press

  18. Jagtap B, Dhotre V (2014) Svm and hmm based hybrid approach of sentiment analysis for teacher feedback assessment. International journal of emerging trends of technology in computer science (IJETCS) 3(3):229–232

    Google Scholar 

  19. Jiang S, Pang G, Wu M, Kuang L (2012) An improved K-nearest-neighbor algorithm for text categorization. Expert Syst Appl 39(1):1503–1509

    Article  Google Scholar 

  20. Kennedy A, Inkpen D (2006) Sentiment classification of movie reviews using contextual valence shifters. Comput Intell 22(2):110–125

    Article  MathSciNet  Google Scholar 

  21. Liu B (2012) Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5(1):1–167

    Article  Google Scholar 

  22. Liu SM, Chen J-H (2015) A multi-label classification based approach for sentiment classification. Expert Syst Appl 42(3):1083–1093

    Article  Google Scholar 

  23. Liu F, Zheng J, Zheng L, Chen C (2020) Combining attention-based bidirectional gated recurrent neural network and two-dimensional convolutional neural network for document-level sentiment classification. Neurocomputing 371:39–50

    Article  Google Scholar 

  24. Luo B, Zeng J, Duan J (2016) Emotion space model for classifying opinions in stock message board. Expert Syst Appl 44:138–146

    Article  Google Scholar 

  25. Matsumoto S, Takamura H, Okumura M (2005) Sentiment classification using word sub-sequences and dependency sub-trees. In: Advances in knowledge discovery and data mining, Springer, pp 301–311

  26. Moraes R, Valiati JF, Neto WPG (2013) Document-level sentiment classification: An empirical comparison between svm and ann. Expert Syst Appl 40 (2):621–633

    Article  Google Scholar 

  27. Niu T, Zhu S, Pang L, El Saddik A (2016) Sentiment analysis on multi-view social data. In: MultiMedia modeling, Springer, pp 15–27

  28. Oreski S, Oreski G (2014) Genetic algorithm-based heuristic for feature selection in credit risk assessment. Expert System with Application 41(4):2052–2064

    Article  Google Scholar 

  29. 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, pp 271

  30. Pang B, Lee L (2005) Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd annual meeting on association for computational linguistics, Ann Arbor, Michigan. Association for Computational Linguistics, pp 115–124

  31. Pang B, Lee L, Vaithyanathan S (2002) Thumbs up?: Sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, Association for Computational Linguistics, pp 79–86

  32. Rao G, Huang W, Feng Z, Cong Q (2018) LSTM With sentence representations for document-level sentiment classification. Neurocomputing 308:49–57

    Article  Google Scholar 

  33. Read J (2005) Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: Proceedings of the ACL student research workshop, Ann Arbor, Michigan. Association for Computational Linguistics, pp 43–48

  34. Refaeilzadeh P, Tang L, Liu H Cross-validation, URL: http://www.public.asu.edu/~ltang9/papers/ency-cross-validation.pdf

  35. Shinde GK, Lokhande VN, Kalyane RT, Gore VB, Raut UM (2021) Sentiment analysis using hybrid approach. International journal for research in applied science and engineering technology (IJRASET) 9:282–285

    Article  Google Scholar 

  36. Tan S, Zhang J (2008) An empirical study of sentiment analysis for chinese documents. Expert Syst Appl 34(4):2622–2629

    Article  Google Scholar 

  37. Tang H, Tan S, Cheng X (2009) A survey on sentiment detection of reviews. Expert Syst Appl 36(7):10760–10773

    Article  Google Scholar 

  38. Tripathy A, Agrawal A, Rath SK (2016) Classification of sentiment reviews using n-gram machine learning approach. Expert System with Application 57:117–126

    Article  Google Scholar 

  39. Tripathy. A, Anand A, Rath SK (2017) Document-level sentiment classification using hybrid machine learning approach. Knowl Inf Syst 53(3):805–831

    Article  Google Scholar 

  40. Wang S, Wei Y, Li D, Zhang W, Li W (2007) A hybrid method of feature selection for chinese text sentiment classification. In: Fourth international conference on fuzzy systems and knowledge discovery, 2007. FSKD 2007, Vol 3, IEEE, pp 435–439

  41. Whitelaw C, Garg N, Argamon S (2005) Using appraisal groups for sentiment analysis. In: Proceedings of the 14th ACM international conference on Information and knowledge management, Bremen, Germany, ACM, pp 625–631

  42. Zhang GP (2000) Neural networks for classification: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part c: Applications and Reviews 30 (4):451–462

    Article  Google Scholar 

  43. Zhang D, Xu H, Su Z, Xu Y (2015) Chinese comments sentiment classification based on word2vec and svm perf. Expert Syst Appl 42(4):1857–1863

    Article  Google Scholar 

  44. Zhu J, Wang H, Mao J (2010) Sentiment classification using genetic algorithm and Conditional Random Fields. In: 2nd IEEE international conference on information management and engineering (ICIME), pp 193–196

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abinash Tripathy.

Ethics declarations

Conflict of Interests

The authors have no conflicts of interest to declare that are relevant to the content of this article. The authors did not receive any financial support from any organization for the submitted work.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tripathy, A., Anand, A. & Kadyan, V. Sentiment classification of movie reviews using GA and NeuroGA. Multimed Tools Appl 82, 7991–8011 (2023). https://doi.org/10.1007/s11042-022-13047-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13047-z

Keywords

Navigation