Text Mining in Digital Libraries using OKAPI BM25 Model

Abstract

The emergence of the internet has made vast amounts of information available and easily accessible online. As a result, most libraries have digitized their content in order to remain relevant to their users and to keep pace with the advancement of the internet. However, these digital libraries have been criticized for using inefficient information retrieval models that do not perform relevance ranking to the retrieved results. This paper proposed the use of OKAPI BM25 model in text mining so as means of improving relevance ranking of digital libraries. Okapi BM25 model was selected because it is a probability-based relevance ranking algorithm. A case study research was conducted and the model design was based on information retrieval processes. The performance of Boolean, vector space, and Okapi BM25 models was compared for data retrieval. Relevant ranked documents were retrieved and displayed at the OPAC framework search page. The results revealed that Okapi BM 25 outperformed Boolean model and Vector Space model. Therefore, this paper proposes the use of Okapi BM25 model to reward terms according to their relative frequencies in a document so as to improve the performance of text mining in digital libraries.

Recovering Scientific Literature on COVID-19 using level-based Text Analysis

“…The authors use the PubMed digital database for Information Retrieval. The performance of the BM25 model was compared with the Boolean model and the vector space model in [17]. The results showed that the documents best ranked as relevant were determined using the OkapiBM25 probability-based ranking algorithm.…”

Analisis Sentimen Aplikasi TikTok menggunakan Metode BM25 dan Improved K-NN Fitur Chi-Square

“…BM25 merupakan metode untuk pemeringkatan yang dapat mencocokan pengurutan antara kata kunci dan dokumen koleksi data yang ada. Menurut penelitian Tinega,dkk 2018 menjelaskan bahwa pemeringkatan penggunaan BM25 mempunyai nilai yang jauh lebih baik jika dibandingkan dengan vector space model dan boolean model [8].…”

Analisis Sentimen Mengenai Moda Raya Terpadu (MRT) Jakarta dengan Metode BM25 dan K-Nearest Neighbor

“…Penelitian tersebut menggunakan data bug report sehingga dapat mendeteksi adanya duplikasi pada bug report dengan akurasi sebesar 90%. Selain itu penelitian (Tinega, et al, 2018) juga memperkuat bahwa pemeringkatan menggunakan BM25 menghasilkan nilai yang jauh lebih baik apabila dibandingankan Boolean Model dan Vector Space Model. Penelitian (Whissel & Clarke, 2013) juga menunjukkan apabila dibandingkan dengan cosinus similarity maka algoritma BM25 merupakan algoritma yang hasil pemeringkatannya lebih baik daripada menggunakan cosinus similarity Hasil akurasi dari penggabungan metode BM25 dengan KNN memiliki hasil akurasi terbaik yaitu 88,97%.…”