International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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SEQUENTIAL MINIMAL OPTIMIZATION WITH RANDOM FOREST ALGORITHM (SMORF) USING TWITTER CLASSIFICATION TECHNIQUES

  • J.Uma (Department of Computer Science Periyar University PG Extension Centre) ;
  • K.Prabha (Department of Computer Science Periyar University PG Extension Centre)
  • 투고 : 2023.04.05
  • 발행 : 2023.04.30
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초록

Sentiment categorization technique be commonly isolated interested in threes significant classifications name Machine Learning Procedure (ML), Lexicon Based Method (LB) also finally, the Hybrid Method. In Machine Learning Methods (ML) utilizes phonetic highlights with apply notable ML algorithm. In this paper, in classification and identification be complete base under in optimizations technique called sequential minimal optimization with Random Forest algorithm (SMORF) for expanding the exhibition and proficiency of sentiment classification framework. The three existing classification algorithms are compared with proposed SMORF algorithm. Imitation result within experiential structure is Precisions (P), recalls (R), F-measures (F) and accuracy metric. The proposed sequential minimal optimization with Random Forest (SMORF) provides the great accuracy.

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참고문헌

  1. A. Rahmatulloh, R. N. Shofa, I. Darmawan and Ardiansah, "Sentiment Analysis of Ojek Online User Satisfaction Based on the Naive Bayes and Net Brand Reputation Method," 2021 9th International Conference on Information and Communication Technology (ICoICT), 2021, pp. 337-341, doi: 10.1109/ICoICT52021.2021.9527466.
  2. D Venkataraman and Mangayarkarasi N, "Computer Vision Based Feature Extraction of Leaves for Identification of Medicinal Values of Plants", IEEE International Conference on Computational Intelligence and Computing Research, 978-1-5090-0612-0/16/$31.00978-1-5090-0612-0/16/$31.00 ©2016 IEEE.
  3. Fire Department of MPSFire Statistical Yearbook of China, Beijing: China Personnel Press,2012.
  4. H. X. Kan, L. Jin, and F. L. Zhou," Classification of Medicinal Plant Leaf Image Based on Multi-Feature Extraction", Pattern Recognition and Image Analysis, Vol. 27, No. 3, 2017, pp. 581-587, 1054-6618. © Pleiades Publishing, Ltd. https://doi.org/10.1134/S105466181703018X
  5. K. PranithaKumari, A. Venugopal Reddy and S.Sameen Fatima, "Web page genre classification: Impact of n-gram lengths," International Journal of Computer Applications, vol. 88(13), February 2014.
  6. Manojkumar P., Surya C. M., and Varun P. Gopi, "Identification of Ayurvedic Medicinal Plants by Image Processing of Leaf Samples", 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp 978-1-5386-1931-5.
  7. Marco Seeland, Michael Rzanny, NedalAlaqraa, Jana Wa ldchen, Patrick Ma der, "Plant species classification using flower images-A comparative study of local feature representations", PLOS ONE | DOI: 10.1371/journal.pone.0170629 February 24, 2017.
  8. Mr. K. Nithiyanandhan and Prof.T. Bhaskara Reddy, "Analysis of the Medicinal Leaves by using Image Processing Techniques and ANN", Vol 8, No. 5, ISSN No. 0976-5697, May-June 2017.
  9. P. Majumder, Kevin Crowston, Marie Williams, "Reproduced and Emergent Genres of Communication on the World-Wide Web", The Information Society, publisher Taylor & Francis, vol. 16(3), pp. 201-215, 2000. https://doi.org/10.1080/01972240050133652
  10. PradeepkumarChoudhary, Rahul Khandekar, AakashBorkar, and PunitChotaliya, "Image processing algorithm for fruit identification", International Research Journal of Engineering and Technology (IRJET), Vol 4 Issue 3, e-ISSN: 2395 -0056, p-ISSN: 2395-0072, Mar -2017.
  11. Pushpa BR, Anand C and MithunNambiar P," Ayurvedic Plant Species Recognition using Statistical Parameters on Leaf Images", International Journal of Applied Engineering Research, Volume 11, Number 7, 2016, 0973-4562.
  12. Riddhi H. Shaparia, Dr.Narendra M. Patel and Prof. Zankhana H. Shah," Flower Classification using Texture and Color Features", International Conference on Research and Innovations in Science, Engineering &Technology, Volume 2, 2017, Pages 113-118.
  13. RuaaAdeebAbdulmunem Al-falluji, "Color ,Shape and Texture based Fruit Recognition System", International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 5, Issue 7, ISSN: 2278 - 1323 ,July 2016.
  14. S. Naz, A. Sharan and N. Malik, "Sentiment Classification on Twitter Data Using Support Vector Machine," 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 2018, pp. 676-679, doi: 10.1109/WI.2018.00-13.
  15. Tseng, Chris &Pateli, N. &Paranjape, Hrishikesh& Lin, T.Y. &Teoh, SooTee. (2012). Classifying twitter data with Naive Bayes Classifier. 294-299. 10.1109/GrC.2012.6468706.
  16. Wei Bi, James T. Kwok, "Efficient Multi-label Classification with Many Labels", Proceedings of the 30th International Conference on Machine Learning (ICML13), pp. 405-413, 2013.