Skip to main content
SpringerLink
Log in

Journal Recommendation System for Author Using Thai and English Information from Manuscript

  • Conference paper
  • First Online:
Proceedings of the 18th International Conference on Computing and Information Technology (IC2IT 2022) (IC2IT 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 453))

Included in the following conference series:

  • 281 Accesses

Abstract

There are thousands of academic journals in various fields of study. An article author must spend significant time searching and selecting a journal suitable for the article’s content before submitting it to a journal for consideration. Since many articles are submitted to a journal at a time, it would take time for an editor to review, submit it to reviewers, and inform the results back to the author. If the article is rejected due to mismatched journal content, the author will spend more time to re-submit the article to another journal. Therefore, this research introduced a recommendation system to help the author choose an appropriate journal more effectively, based on TCI Thai Journals Online Database (ThaiJO). Data from Thai and English articles were used for analysis in this research. Our work involved studying the applied data, cleaning the data, and modeling, which included calculating the importance of text by Term Frequency - Inverse Document Frequency (TF-IDF), calculating similarity scores between articles and journals using Cosine Similarity and then ranking the scores to recommend the most suitable journal. This research experiment with modeling between a model from Thai data, a model from English data, and a model using both languages. The experiment shows that when we combine Thai and English keywords and abstract data, the accuracy in the form of hit rate is improved to 0.80650 from applying only English (0.78793) or Thai data (0.62888).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Scopus. https://www.elsevier.com/solutions/scopus/how-scopus-works/content. Accessed 10 Oct 2021

  2. ThaiJo. https://www.tci-thaijo.org/. Accessed 21 Oct 2021

  3. Haddi, E., Liu, X., Shi, Y.: The role of text pre-processing in sentiment analysis. Procedia Comput. Sci. 17, 26–32 (2013)

    Article  Google Scholar 

  4. Sornlertlamvanich, V.: Word segmentation for Thai in machine translation system. Mach. Transl. NECTEC, 556–561 (1993)

    Google Scholar 

  5. PyThaiNLP. https://pythainlp.github.io/docs/2.3/. Accessed 12 Aug 2021

  6. Robertson, S.: Understanding inverse document frequency: on theoretical arguments for IDF. J. Doc. 60(5), 503–520 (2004). https://doi.org/10.1108/00220410410560582

    Article  Google Scholar 

  7. Mizzaro, S., Pavan, M., Scagnetto, I.: Content-based similarity of Twitter users. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds.) ECIR 2015. LNCS, vol. 9022, pp. 507–512. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16354-3_56

    Chapter  Google Scholar 

  8. Li, B., Han, L.: Distance weighted cosine similarity measure for text classification. In: Yin, H., et al. (eds.) IDEAL 2013. LNCS, vol. 8206, pp. 611–618. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41278-3_74

    Chapter  Google Scholar 

  9. Moreno-Torres, J.G., Saez, J.A., Herrera, F.: Study on the impact of partition-induced dataset shift on k-fold cross-validation. IEEE Trans. Neural Netw. Learn. Syst. 23(8), 1304–1312 (2012). https://doi.org/10.1109/TNNLS.2012.2199516

    Article  Google Scholar 

  10. Magara, M.B., Ojo, S.O., Zuva, T.: A comparative analysis of text similarity measures and algorithms in research paper recommender systems. In: 2018 Conference on Information Communications Technology and Society (ICTAS), pp. 1–5 (2018)

    Google Scholar 

  11. Lee, J., Lee, K., Kim, J.: Personalized academic research paper recommendation system. arXiv:1304.5457 (2013)

  12. Sugiyama, K., Kan, M.Y.: Scholarly paper recommendation via user’s recent research interests. In: Proceedings of the 10th Annual Joint Conference on Digital Libraries, Gold Coast, Queensland, Australia, pp. 29–38. Association for Computing Machinery (2010)

    Google Scholar 

  13. Stopwords ISO. https://github.com/stopwords-iso/stopwords-iso. Accessed 4 Aug 2021

  14. Kuhn, M., Johnson, K.: Over-fitting and model tuning. In: Kuhn, M., Johnson, K. (eds.) Applied Predictive Modeling, pp. 61–92. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-6849-3_4

    Chapter  MATH  Google Scholar 

  15. James, G., Witten, D., Hastie, T., Tibshirani, R.: Resampling methods. In: James, G., Witten, D., Hastie, T., Tibshirani, R. (eds.) An Introduction to Statistical Learning: with Applications in R, pp. 175–201. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-7138-7_5

    Chapter  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand

    Nithirun Numnonda & Nuengwong Tuaycharoen

  2. National Electronics and Computer Technology Center (NECTEC), Bangkok, Thailand

    Sapa Chanyachatchawan

Authors
  1. Nithirun Numnonda
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sapa Chanyachatchawan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nuengwong Tuaycharoen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nithirun Numnonda .

Editor information

Editors and Affiliations

  1. Faculty of IT and Digital Innovation, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand

    Phayung Meesad

  2. Faculty of IT and Digital Innovation, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand

    Sunantha Sodsee

  3. Faculty of IT and Digital Innovation, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand

    Watchareewan Jitsakul

  4. Faculty of IT and Digital Innovation, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand

    Sakchai Tangwannawit

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Numnonda, N., Chanyachatchawan, S., Tuaycharoen, N. (2022). Journal Recommendation System for Author Using Thai and English Information from Manuscript. In: Meesad, P., Sodsee, S., Jitsakul, W., Tangwannawit, S. (eds) Proceedings of the 18th International Conference on Computing and Information Technology (IC2IT 2022). IC2IT 2022. Lecture Notes in Networks and Systems, vol 453. Springer, Cham. https://doi.org/10.1007/978-3-030-99948-3_14

Download citation

Publish with us

Policies and ethics

Search

Navigation