A new opinion mining method based on M-cuckoo with  M-cat and SVM with NB classification algorithm

M Malathi; Antony Selvadoss Thanamani; P Padmapriya; S. Sharmila; A. Finny Belwin; A. Linda Sherin

doi:10.53730/ijhs.v6nS2.8353

A new opinion mining method based on M-cuckoo with M-cat and SVM with NB classification algorithm

https://doi.org/10.53730/ijhs.v6nS2.8353

Authors

Malathi M
malathi.mca09@gmail.com
Research Scholar, Department of Computer Science, Nallamuthu Gounder Mahalingam College, Pollachi, Coimbatore, Tamilnadu, India
Antony Selvadoss Thanamani Head & Associate Professor, Department of Computer Science, Nallamuthu Gounder Mahalingam College, Pollachi, Coimbatore, Tamilnadu, India
Padmapriya. P Research Scholar, Department of Computer Science, Nallamuthu Gounder Mahalingam College, Pollachi, Coimbatore, Tamilnadu, India
S. Sharmila Research Scholar, Department of Computer Science, Nallamuthu Gounder Mahalingam College, Pollachi, Coimbatore, Tamilnadu, India
A. Finny Belwin Research Scholar, Department of Computer Science, Nallamuthu Gounder Mahalingam College, Pollachi, Coimbatore, Tamilnadu, India
A. Linda Sherin Research Scholar, Department of Computer Science, Nallamuthu Gounder Mahalingam College, Pollachi, Coimbatore, Tamilnadu, India

Keywords:

cuckoo search, opinion mining, swarm intelligence, classification, decision making, positive, negative

Abstract

A different point of view can help you make better decisions. People exchange useful knowledge with others thanks to the advent of social media via the Internet. This knowledge is utilised to coordinate, explore, and analyse for better decision making. Opinion Mining is a task in Natural Language Processing that involves defining and extracting information in order to analyse a person's attitude. The basic goal of opinion mining is to determine the polarity of a review at the phrase level, and whether the transmitted opinion is favourable or negative. Metaheuristic algorithms are currently widely utilised in data mining, engineering designs, image processing, and machine learning optimization. These algorithms are typically simple and adaptable, straightforward to implement, and practical. Some fields remain undeveloped.

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References

Arti Buche, Dr. M. B. Chandak, "Opinion Mining and Analysis: A Survey", International Journal on Natural Language Computing (IJNLC) Vol. 2, No.3, June 2013.

Asad Ullah Rafiq Khan, Madiha Khan et al., "Naïve Multi-label classification of YouTube comments using comparative opinion mining", Elsevier, Procedia Computer Science 82 (2016), pp.57-64.

Edison Marrese-Taylor, Juan D. Vel´asquez et al., "Identifying Customer Preferences about Tourism Products using an Aspect-Based Opinion Mining Approach", 17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems - KES2013, Science Direct.

G.K. Shailajaand C.V. Guru Rao, "Opposition Intensity-Based Cuckoo Search Algorithm for Data Privacy Preservation", J. Intell. Syst. 2020; 29(1): 1441–1452.

Iztok Fister Jr, Dusan Fister et al., "A comprehensive review of cuckoo search: variants and hybrids", Int. J. Mathematical Modelling and Numerical Optimisation, Vol. 4, No. 4, 2013, pp. 387-409.

K. Sowmya, K. Monika et al., "Customer Review Rating Analysis Using Opinion Mining", International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-7, May, 2019.

Khalid Ait Hadi, Rafik Lasri et al., "An Efficient Approach for Sentiment Analysis in a Big Data Environment", International Journal of Engineering and Advanced Technology (IJEAT), ISSN: 2249-8958, Volume-8 Issue-4, April 2019.

Manar Abdulkareem Al-Abaji, "A Literature Review of Cuckoo Search Algorithm", Journal of Education and Practice, ISSN 2222-1735 (Paper) ISSN 2222-288X (Online), Vol.11, No.8, 2020.

S. K. Lakshmanaprabu, K. Shankar et al., "Ranking Analysis for Online Customer Reviews of Products Using Opinion Mining with Clustering", Hindawi, Complexity, Volume 2018, Article ID 3569351, 9 pages.

Venkata Vijaya Geeta. Pentapalli, Ravi Kiran Varma P, "Cuckoo Search Optimization and its Applications: A Review", International Journal of Advanced Research in Computer and Communication Engineering ISO 3297:2007 Certified Vol. 5, Issue 11, November 2016.

Published

03-06-2022

How to Cite

Malathi, M., Thanamani, A. S., Padmapriya, P., Sharmila, S., Belwin, A. F., & Sherin, A. L. (2022). A new opinion mining method based on M-cuckoo with M-cat and SVM with NB classification algorithm. International Journal of Health Sciences, 6(S2), 12776–12785. https://doi.org/10.53730/ijhs.v6nS2.8353

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Issue

Vol. 6 No. S2 (2022)

Section

Peer Review Articles

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