Text Mining Approach for Topic Modeling of Corpus Al Qur'an in Indonesian Translation

Rolliawati, Dwi; Rozas, Indri Sudanawati; Khalid,; Ratodi, Muhamad

doi:10.31227/osf.io/b4z76

2018

DOI: 10.31227/osf.io/b4z76

|View full text |Cite

Preprint

Text Mining Approach for Topic Modeling of Corpus Al Qur'an in Indonesian Translation

Dwi Rolliawati¹,

Indri Sudanawati Rozas²,

Khalid Khalid³

et al.

Abstract: Abstract-The Qur'an is the religious text for Muslims that is revealed to humanity as a guide to solve any problems in all aspects ilmu), charity (amal), and jihad, 4) Day (siang), night (malam), life (hidup), and death (mati). Those four topics were related to the moderator variables associated with the revelation location of Quranic verses (Makki and Madani

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2020

Publication Types

Select...

Book1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Quranic Topic Modelling Using Paragraph Vectors

Alshammeri

Atwell

Alsalka

2020

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

The Quran is known for its linguistic and spiritual value. It comprises knowledge and topics that govern different aspects of people's life. Acquiring and encoding this knowledge is not a trivial task due to the overlapping of meanings over its documents and passages. Analysing a text like the Quran requires learning approaches that go beyond word level to achieve sentence level representation. Thus, in this work, we follow a deep learning approach: paragraph vector to learn an informative representation of Quranic Verses . We use a recent breakthrough in embeddings that maps the passages of the Quran to vector representation that preserves more semantic and syntactic information. These vectors can be used as inputs for machine learning models, and leveraged for the topic analysis. Moreover, we evaluated the derived clusters of related verses against a tagged corpus, to add more significance to our conclusions. Using the paragraph vectors model, we managed to generate a document embedding space that model and explain word distribution in the Holy Quran. The dimensions in the space represent the semantic structure in the data and ultimately help to identify main topics and concepts in the text.

show abstract

Quranic Topic Modelling Using Paragraph Vectors

Alshammeri

Atwell

Alsalka

2020

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Text Mining Approach for Topic Modeling of Corpus Al Qur'an in Indonesian Translation

Cited by 1 publication

References 8 publications

Quranic Topic Modelling Using Paragraph Vectors

Quranic Topic Modelling Using Paragraph Vectors

Contact Info

Product

Resources

About