AccScience Publishing / IJOSI / Volume 6 / Issue 5 / DOI: 10.6977/IJoSI.202109_6(5).0006
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ARTICLE

A Text Analytics Approach to Study Python Questions Posted on Stack Overflow

Lee Yong Meng1 ,  Soo Yin Yi1 ,  Gan Keng Hoon1* ,  Nur-Hana Samsudin1
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1 School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang , MALAYSIA
IJOSI 2020, 6(5), 55–66; https://doi.org/10.6977/IJoSI.202109_6(5).0006
Received: 13 July 2021 | Revised: 1 September 2021 | Accepted: 9 September 2021 |
© Invalid date by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Stack Overflow (SO) is one of the largest discussion platforms for programmers to communicate their ideas and thoughts related to various topics like software development and data analysis. Many programmers are actively contributing to this platform and discuss about Python programming language. To better study the topics related to Python questions posted on the platform, a text analytics approach incorporating text preprocessing steps and Latent Dirichlet Allocation (LDA) topic modelling algorithm is proposed. The two main objectives of this study are: to discover and compare the topics of the questions about Python programming language posted on SO from 2008 to 2016, and to analyze questions about Python programming language with high votes posted on SO from 2008 to 2016 using topic modelling technique with a suitable number of topics. From the study, we find that the topics of the Python questions posted on Stack Overflow have gradually shifted towards those related to data modelling and analysis from 2008 to 2016. Furthermore, the study also shows that a suitable number of topics using the topic modelling technique yield a high coherence score concerning the topic model in use, which is important to extract more meaningful topics from the collection of Python questions.

Keywords
Stack Overflow
text processing
text analytics
topic modelling
References
  1. Ahmad, A., Feng, C., Li, K., Asim, S. M., & Sun, T. (2019). Toward empirically investigating non-functional requirements of iOS developers on stack overflow. IEEE Access, 7, 61145–61169.
  2. Alghamdi, R., & Alfalqi, K. (2015). A survey of topic modeling in text mining. Int. J. Adv. Comput. Sci. Appl. (IJACSA), 6.
  3. Ali, R. H., & Linstead, E. (2020). Modeling Topic Exhaustion for Programming Languages on StackOverflow. SEKE, (pp. 400–405).
  4. Beyer, S., Macho, C., Di Penta, M., & Pinzger, M. (2020). What kind of questions do developers ask on Stack Overflow? A comparison of automated approaches to classify posts into question categories. Empirical Software Engineering, 25, 2258–2301.
  5. Chakraborty, P., Shahriyar, R., Iqbal, A., & Uddin, G. (2021). How do developers discuss and support new programming languages in technical Q&A site? An empirical study of Go, Swift, and Rust in Stack Overflow. Information and Software Technology, 137, 106603.
  6. Curiskis, S. A., Drake, B., Osborn, T. R., & Kennedy, P. J. (2020). An evaluation of document clustering and topic modelling in two online social networks: Twitter and Reddit. Information Processing & Management, 57, 102034.
  7. Dirkson, A. R., Verberne, S., Kraaij, W., Jorge, A. M., Campos, R., Jatowt, A., & Bhatia, S. (2019). Narrative detection in online patient communities. Proceedings of Text2Story—Second Workshop on Narrative Extraction From Texts co-located with 41th European Conference on Information Retrieval (ECIR 2019), (pp. 21–28).
  8. Fontão, A., Ábia, B., Wiese, I., Estácio, B., Quinta, M., dos Santos, R. P., & Dias-Neto, A. C. (2018). Supporting governance of mobile application developers from mining and analyzing technical questions in stack overflow. Journal of Software Engineering Research and Development, 6, 1–34.
  9. Jaworska, S., & Nanda, A. (2018). Doing well by talking good: A topic modelling-assisted discourse study of corporate social responsibility. Applied Linguistics, 39, 373–399.
  10. Karbasian, H., & Johri, A. (2020). Insights for curriculum development: Identifying emerging data science topics through analysis of Q&A communities. Proceedings of the 51st ACM Technical Symposium on Computer Science Education, (pp. 192–198).
  11. Kaur, J., & Buttar, P. K. (2018). A systematic review on stopword removal algorithms. International Journal on Future Revolution in Computer Science & Communication Engineering, 4, 207–210.
  12. Marçal, I., Garcia, R. E., Eler, D., & Correia, R. C. (2020). A Strategy to Enhance Computer Science Teaching Material Using Topic Modelling: Towards Overcoming The Gap Between College And Workplace Skills. Proceedings of the 51st ACM Technical Symposium on Computer Science Education, (pp. 366–371).
  13. Meldrum, S., Licorish, S. A., Owen, C. A., & Savarimuthu, B. T. (2020). Understanding stack overflow code quality: A recommendation of caution. Science of Computer Programming, 199, 102516.
  14. Moessner, M., Feldhege, J., Wolf, M., & Bauer, S. (2018). Analyzing big data in social media: Text and network analyses of an eating disorder forum. International Journal of Eating Disorders, 51, 656–667.
  15. Overflow, S. (2019, 10). Python Questions from Stack Overflow. Python Questions from Stack Overflow. Retrieved from https://www.kaggle.com/stackoverflow/pythonquestions
  16. Rahate, P. M., & Chandak, M. (2019). Text Normalization and Its Role in Speech Synthesis. International Journal of Engineering and Advanced Technology Special Issue, 8, 115–122. doi:10.35940/ijeat.e1029.0785s319
  17. Röder, M., Both, A., & Hinneburg, A. (2015). Exploring the space of topic coherence measures. Proceedings of the eighth ACM international conference on Web search and data mining, (pp. 399–408).
  18. Tamla, P., Böhm, T., Nawroth, C., Hemmje, M., & Fuchs, M. (2019). What Do Serious Games Developers Search Online? A Study of GameDev StackExchange. CERC, (pp. 131–142).
  19. Wang, L., Zhang, L., & Jiang, J. (2020). Duplicate question detection with deep learning in stack overflow. IEEE Access, 8, 25964–25975.
  20. Xu, L., Nian, T., & Cabral, L. (2020). What makes geeks tick? a study of stack overflow careers. Management Science, 66, 587–604.
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International Journal of Systematic Innovation, Electronic ISSN: 2077-8767 Print ISSN: 2077-7973, Published by AccScience Publishing
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