Visualizing sharia law: an information retrieval approach based on key extraction algorithm

Islamic jurisprudence as it relates to every element of Muslim life that founds in Sharia. Many interpretations exist for Sharia, all based on differing Islamic schools of thought and multiple laws concerning Sharia. In Malaysia, there are 135 sections of Sharia law related to family and marriage in Act 303. The survey conducted proves that Malaysians struggled when looking for sharia law and took a toll of time. Therefore, we propose a web-based Sharia Law Finder (SLF) system that solves the sharia law’s information retrieval issues in Malaysia. The SLF adapted the key extraction (KE) algorithm, which is Term Frequency-Inverse Document Frequency (TF-IDF), and Rapid Automatic Keyword Extraction (RAKE). The testing for implementation of both algorithm effectiveness is using the functional and usability test. The solution enhances using visualization tools which are bubble charts and word cloud. Bubble charts visualize the related Sharia law based on users’ queries and word cloud to visualize keywords used by the users. A total of 30 respondents have tested the functionality and usability of SLF. As a result, the system successfully works as specified functionality, 96.58% for the System Usability Scale, indicating the proposed solution’s acceptance.

[1]  Zongda Wu,et al.  A dummy-based user privacy protection approach for text information retrieval , 2020, Knowl. Based Syst..

[2]  A. Akinniyi.  Islamic Identity , 2019, SSRN Electronic Journal.

[3]  Mohammed Erritali.  Information Retrieval: Textual Indexing Using an Oriented Object Database , 2016 .

[4]  Shweta Ganiger,et al.  Comparative Study on Keyword Extraction Algorithms for Single Extractive Document , 2018, 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS).

[5]  Hyder Gulam.  THE APPLICATION OF SHARIAH (ISLAMIC LAW) IN SOME DIFFERENT COUNTRIES AND ITS IMPLICATIONS , 2016 .

[6]  M T Artese,et al.  What is this painting about? Experiments on Unsupervised Keyphrases Extraction algorithms , 2018, IOP Conference Series: Materials Science and Engineering.

[7]  W. Khairuldin,et al.  The Binding of Laws to Personal Opinion of Muftis in Malaysia , 2018, International Journal of Academic Research in Business and Social Sciences.

[8]  Amirah Ismail,et al.  Semantic-Based Information Retrieval for Java Learning Management System , 2014 .

[9]  Shibamouli Lahiri,et al.  Keywords at Work: Investigating Keyword Extraction in Social Media Applications , 2018 .

[10]  Vimala Balakrishnan,et al.  Stemming and lemmatization: A comparison of retrieval performances , 2014 .

[11]  Ricardo Campos,et al.  YAKE! Collection-Independent Automatic Keyword Extractor , 2018, ECIR.

[12]  Giuseppe Sansonetti,et al.  Social semantic query expansion , 2013, ACM Trans. Intell. Syst. Technol..

[13]  Michael W. Berry,et al.  Text mining : applications and theory , 2010 .

[14]  David E. Losada,et al.  Effective sentence retrieval based on query-independent evidence , 2012, Inf. Process. Manag..

[15]  Juan Ramos-González,et al.  A Text Mining-Based Approach for Analyzing Information Retrieval in Spanish: Music Data Collection as a Case Study , 2018, DISC 2018.

[16]  Raihah Aminuddin,et al.  TF-IDF and Data Visualization For Syafie Madhhab Hadith Scriptures Authenticity , 2021, 2021 IEEE 11th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE).

[17]  M. Muhammed,et al.  The shariah and law : An analysis , 2017 .

[18]  Saeedeh Momtazi,et al.  Text‐based question answering from information retrieval and deep neural network perspectives: A survey , 2020, WIREs Data Mining Knowl. Discov..

[19]  M. Ebrahimi,et al.  Islamic Identity, Ethical Principles and Human Values , 2017 .

[20]  Lucía Martín Gómez,et al.  A Text Mining-Based Approach for Analyzing Information Retrieval in Spanish: Music Data Collection as a Case Study , 2018, DCAI.

[21]  Andino Maseleno,et al.  Charismatic Document Clustering Through Novel K-Means Non-negative Matrix Factorization (KNMF) Algorithm Using Key Phrase Extraction , 2018, International Journal of Parallel Programming.

[22]  Shahzad Qaiser,et al.  Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents , 2018, International Journal of Computer Applications.

[23]  Jack Belzer,et al.  Data structures for information retrieval , 1972, SIGF.

[24]  Prawitra Thalib.  DISTINCTION OF CHARACTERISTICS SHARIA AND FIQH ON ISLAMIC LAW , 2018, Yuridika.

[25]  Md. Haider Ali,et al.  An empirical study of important keyword extraction techniques from documents , 2017, 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM).

[26]  Feifan Liu,et al.  Unsupervised Approaches for Automatic Keyword Extraction Using Meeting Transcripts , 2009, NAACL.

[27]  Aditi Sharan,et al.  Keyword and Keyphrase Extraction Techniques: A Literature Review , 2015 .

[28]  D. Ebert,et al.  Dimensionality of the system usability scale among professionals using internet-based interventions for depression: a confirmatory factor analysis , 2020, BMC Psychiatry.

[29]  Alen Doko,et al.  Improved sentence retrieval using local context and sentence length , 2013, Inf. Process. Manag..

[30]  S. Chitrakala,et al.  A survey on extractive text summarization , 2017, 2017 International Conference on Computer, Communication and Signal Processing (ICCCSP).

[31]  Dipankar Chaki,et al.  A Novel Approach to Extract Important Keywords from Documents Applying Latent Semantic Analysis , 2018, 2018 10th International Conference on Knowledge and Smart Technology (KST).

[32]  Dhanya Pramod,et al.  Document clustering: TF-IDF approach , 2016, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).