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Article

Credibility Analysis of User-Designed Content Using Machine Learning Techniques

by
Milind Gayakwad
1,*,
Suhas Patil
1,
Amol Kadam
1,
Shashank Joshi
1,
Ketan Kotecha
2,
Rahul Joshi
2,*,
Sharnil Pandya
2,
Sudhanshu Gonge
2,
Suresh Rathod
2,
Kalyani Kadam
2 and
Maya Shelke
3
1
Bharati Vidyapeeth Deemed to Be University, College of Engineering, Pune 411043, India
2
Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India
3
Jaywant Shikshan Prasarak Mandal’s Rajarshi Shahu College of Engineering, Pune 411046, India
*
Authors to whom correspondence should be addressed.
Appl. Syst. Innov. 2022, 5(2), 43; https://doi.org/10.3390/asi5020043
Submission received: 28 December 2021 / Revised: 24 March 2022 / Accepted: 6 April 2022 / Published: 14 April 2022
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Abstract

Content is a user-designed form of information, for example, observation, perception, or review. This type of information is more relevant to users, as they can relate it to their experience. The research problem is to identify the credibility and the percentage of credibility as well. Assessment of such content is important to convey the right understanding of the information. Different techniques are used for content analysis, such as voting the content, Machine Learning Techniques, and manual assessment to evaluate the content and the quality of information. In this research article, content analysis is performed by collecting the Movie Review dataset from Kaggle. Features are extracted and the most relevant features are shortlisted for experimentation. The effect of these features is analyzed by using base regression algorithms, such as Linear Regression, Lasso Regression, Ridge Regression, and Decision Tree. The contribution of the research is designing a heterogeneous ensemble regression algorithm for content credibility score assessment, which combines the above baseline methods. Moreover, these factors are also toned down to obtain the values closer to Gradient Descent minimum. Different forms of Error Loss, such as Mean Absolute Error, Mean Squared Error, LogCosh, Huber, and Jacobian, and the performance is optimized by introducing the balancing bias. The accuracy of the algorithm is compared with induvial regression algorithms and ensemble regression separately; this accuracy is 96.29%.
Keywords: content analysis; the credibility of content based on score; regression loss analysis content analysis; the credibility of content based on score; regression loss analysis

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MDPI and ACS Style

Gayakwad, M.; Patil, S.; Kadam, A.; Joshi, S.; Kotecha, K.; Joshi, R.; Pandya, S.; Gonge, S.; Rathod, S.; Kadam, K.; et al. Credibility Analysis of User-Designed Content Using Machine Learning Techniques. Appl. Syst. Innov. 2022, 5, 43. https://doi.org/10.3390/asi5020043

AMA Style

Gayakwad M, Patil S, Kadam A, Joshi S, Kotecha K, Joshi R, Pandya S, Gonge S, Rathod S, Kadam K, et al. Credibility Analysis of User-Designed Content Using Machine Learning Techniques. Applied System Innovation. 2022; 5(2):43. https://doi.org/10.3390/asi5020043

Chicago/Turabian Style

Gayakwad, Milind, Suhas Patil, Amol Kadam, Shashank Joshi, Ketan Kotecha, Rahul Joshi, Sharnil Pandya, Sudhanshu Gonge, Suresh Rathod, Kalyani Kadam, and et al. 2022. "Credibility Analysis of User-Designed Content Using Machine Learning Techniques" Applied System Innovation 5, no. 2: 43. https://doi.org/10.3390/asi5020043

APA Style

Gayakwad, M., Patil, S., Kadam, A., Joshi, S., Kotecha, K., Joshi, R., Pandya, S., Gonge, S., Rathod, S., Kadam, K., & Shelke, M. (2022). Credibility Analysis of User-Designed Content Using Machine Learning Techniques. Applied System Innovation, 5(2), 43. https://doi.org/10.3390/asi5020043

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