Review Sentiment Classification and Feature Selection Using Hybridized Support Vector Machine-Reference-Cited by-同舟云学术

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Review Sentiment Classification and Feature Selection Using Hybridized Support Vector Machine

Published:2023 Issue: Volume: Page:327-337
ISSN:2524-7565
Container-title:Algorithms for Intelligent Systems
language:
Short-container-title:

Author:

Jena Alok Kumar,Gopal K. Murali,Tripathy A.,Panda Nibedan

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-3485-0_25

Reference15 articles.

1. Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1–8

2. Wang Y, Wang XJ (2005) A new approach to feature selection in text classification. In: 2005 international conference on machine learning and cybernetics, vol 6. IEEE, pp 3814–3819

3. Vapnik V (1999) The nature of statistical learning theory. Springer science & business media

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

5. Prastyo PH, Hidayat R, Ardiyanto I (2022) Enhancing sentiment classification performance using hybrid query expansion ranking and binary particle swarm optimization with adaptive inertia weights. ICT Express 8(2):189–197

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Sentiment analysis of reviews using Nature Inspired PSO and ANN;2023 Global Conference on Information Technologies and Communications (GCITC);2023-12-01

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