Feature Selection by Associativity for Sentiment Analysis-Reference-Cited by-同舟云学术

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Feature Selection by Associativity for Sentiment Analysis

Published:2021 Issue: Volume: Page:423-430
ISSN:2190-3018
Container-title:Smart Computing Techniques and Applications
language:
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Author:

Sayeedunnisa S. Fouzia,Hegde Nagaratna P.,Khan Khaleel Ur Rahman

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-0878-0_42

Reference22 articles.

1. Wu, Y., Ren, F.: Learning sentimental influence in twitter. In: 2011 International Conference on Future Computer Sciences and Application, pp. 119–122. IEEE (2011)

2. Chung, W., Tseng, T.L.B.: Discovering business intelligence from online product reviews: a rule-induction framework. Expert Syst. Appl. 39(15), 11870–11879 (2012)

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

4. Bhatnagar, V., Goyal, M., Hussain, M.A.: A novel aspect based framework for tourism sector with improvised aspect and opinion mining algorithm. Int. J. Rough Sets Data Anal. (IJRSDA) 5(2), 119–130 (2018)

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

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1. Augmented GPS Navigation: Enhancing the Reliability of Location-Based Services;2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT);2023-05-05

2. Impact of e-Learning in Education Sector: A Sentiment Analysis View;2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI);2022-12-21

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