Pengaruh Distribusi Panjang Data Teks pada Klasifikasi: Sebuah Studi Awal

Authors

  • Said Al Faraby Telkom University, Bandung
  • Ade Romadhony Telkom University, Bandung

DOI:

https://doi.org/10.30865/mib.v6i3.4259

Keywords:

Text Classification, Cross-Length

Abstract

In text classification, there is a problem with text domain differences (cross-domain) between the data used to train the model and the data used when the model is applied. In addition to the problem of domain differences, there are also language differences (cross-lingual). Many previous studies have looked for ways how classification models can be applied effectively and efficiently in these cross-domain and cross-lingual situations. However, there is one difference that is not given special attention because it is considered not very influential, namely the difference in text length (cross-length). In this study, we further investigated the cross-length condition by creating a special dataset and testing it with various commonly used classification models. The results showed that the difference in the distribution of text length between the training data and the test data could affect the performances. Cross-length transfers from long to short texts show an average decrease in F1-scores across all models of 14%, while transfers from short to long texts give an average decrease of 9%.

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Published

2022-07-25

Issue

Vol. 6 No. 3 (2022): Juli 2022

Section

Articles

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