Autonomous Tagging of Stack Overflow Questions

Authors

  • B. Ravinder Reddy  Assistant Professor, Department of CSE, Anurag University, Hyderabad, T.S., India
  • K. Bhadri Prabhav  Department of CSE, Anurag University, Hyderabad, T.S., India
  • P. Hemanth Sagar  Department of CSE, Anurag University, Hyderabad, T.S., India
  • P. Rahul  Department of CSE, Anurag University, Hyderabad, T.S., India

DOI:

https://doi.org/10.32628/IJSRST52310240

Keywords:

Tagging autonomously, stack overflow

Abstract

Educational resources like question-and-answer websites like Stack Exchange and Quora are growing in popularity online. A large number of these gatherings depend on labeling, which includes a part marking a post with a suitable assortment of subjects that depict the post and make it more straightforward to find and sort. We give a multi-name order framework that naturally distinguishes clients' requests to upgrade the client experience. A straight SVM and a carefully selected portion of the researched highlight set are used to create a one-versus-rest classifier for a Stack Overflow dataset. By utilizing a subsample of the initial data that is restricted to 100 labels and at least 500 events of each label throughout the data, our characterization framework achieves an ideal F1 score of 62.35 percent.

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International Journal of Scientific Research in Science and Technology

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Published

2023-04-30

Issue

Volume 10, Issue 2, March-April-2023

Section

Research Articles

License

Copyright (c) IJSRST

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
B. Ravinder Reddy, K. Bhadri Prabhav, P. Hemanth Sagar, P. Rahul "Autonomous Tagging of Stack Overflow Questions" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 10, Issue 2, pp.263-269, March-April-2023. Available at doi : https://doi.org/10.32628/IJSRST52310240