Arabic words clustering by using K-means algorithm

Al-Azzawy, Dhyaa Shaheed; Alrufaye, Faiez Musa Lahmood

doi:10.1109/ntict.2017.7976098

Cited by 10 publications

(8 citation statements)

References 5 publications

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“…This test concludes that the proposed map-based similarity measurement in clustering document selection is highly promising because it provides a more consistent clustering than human structures. This information was obtained from Al-Azzawy and Al-Rufaye, 2017 [119], which is the reference that you should use for the source. The words in a piece of written work may be organized into clusters using a technique that was developed by taking into account a number of criteria, including their morphological, syntactic, and semantic similarities.…”

Section: Related Workmentioning

confidence: 99%

Document Clustering in the Age of Big Data: Incorporating Semantic Information for Improved Results

Haji¹,

Al-Zebari²,

Şengür³

et al. 2023

JASTT

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There has been a meteoric rise in the total amount of digital texts as a direct result of the proliferation of internet access. As a direct result of this, document clustering has evolved into a crucial method that must be used in order to successfully extract relevant information from big document collections. When employing the document clustering approach, documents are automatically sorted into groups whose members have a high degree of similarity to one another. These groups are created by applying the document clustering technique. Because they do not take into account the semantic linkages that exist between the texts, traditional clustering approaches are unable to provide an acceptable description of a collection of texts. This is because traditional clustering techniques. Document clusters, in which texts are ordered according to their meaning rather than their use of keywords, have been extensively utilized as a means of overcoming these challenges as a result of the incorporation of semantic information. This has been possible as a result of the fact that document clusters can group together related texts. In this investigation, we looked at a total of 27 distinct papers that were published over the previous five years and categorized the documents based on the semantic similarities that existed between the various pieces. A detailed literature evaluation is included to each and every one of the publications that were selected for further consideration. Comparative research is carried out on a wide variety of evaluation strategies, including as algorithms, similarity metrics, instruments, and processes. Following that, there is a drawn-out discussion that analyzes the similarities and differences between the activities.

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Section: Related Workmentioning

confidence: 99%

Document Clustering in the Age of Big Data: Incorporating Semantic Information for Improved Results

Haji¹,

Al-Zebari²,

Şengür³

et al. 2023

JASTT

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“…To show the effectiveness and performance of the proposed system, it must be objectively evaluated. There are four measurements of evaluation: precision, recall, f-measure and accuracy by using Equations 2, 3, 4 and 5, which they have two types of corrects (TP and TN) and two types of errors (FP and FN) [13][14][15] 1. True positive (TP): it's the number of words that have been given the label (1) by the expert and system.…”

Section: Evaluation Technique Of the Proposed Systemmentioning

confidence: 99%

Automated Analysis of Comments on Press Articles in Websites (Articles Literature as Case Study)

Abdulrahman¹,

Rahma²

2020

ETJ

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Due to the increasing electronic publishing sites for printed and certified newspapers, the reader faced the problem of reaching his goal by accessing these sites, which led to the neglect of a large section of important publications. The provision of an automated measure to verify the positive and negative articles based on the analysis of readers' comments on the articles is a necessity to see the important articles that are compatible with the corpus generated by us for inference. The project achieved the previous target and achieved a success rate. The Bag-of-Words Model (BoW) was used to obtain the repetition of the block of words to build the corpus. The proposed system is evaluated based on four metrics (Accuracy= 93%, Precision= 94%, Recall= 94% and F-measure= 94%). The effectiveness results obtain by this system was (Accuracy, Precision and Recall and F-measure).

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“…Second, the research in Arabic tweets is still in its beginning; therefore, there is an area to contribute and improve the Arabic text clustering. However, it might be challenging to handle Arabic text, since there is a lack in Arabic corpus [6]. Third, researchers successfully cluster text using TF-IDF functions, then the k-means algorithm, which proved to be successful even in Arabic text.…”

Section: Related Workmentioning

confidence: 99%

“…The purpose of preprocessing is to take the data (tweets) through multiple phases. In each phase, we deal with a specific technique until reaching the final phase which will be ready for analysis [6]. Performing preprocessing techniques is necessary to only keep relevant information in the tweet.…”

Section: Preprocessingmentioning

confidence: 99%

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Clustering Arabic Tweets for Saudi National Vision 2030

Ferwana¹

2019

IJATCSE

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Twitter provides a valuable resource for opinion mining, which many applications can take place in investigating peoples' interests and concerns. In this paper, we are interested to investigate users' main concerns regarding the national Saudi vision of 2030. As the vision has many tracks of planning, we investigated the education and health sectors. The aim of this study is to show people's focus and interest in implementing this vision. For this, some unsupervised Machine Learning techniques are implemented. Three clustering algorithms are experimented (K-means, Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF)). LDA and NMF give promising results, as both clustered the data into two significant discriminative clusters with a silhouette coefficient equals to 0.639 and 0.538 respectively. While K-means provides overlapped clusters with a silhouette coefficient equals to 0.181. Therefore, our models are able to cluster Arabic tweets in the context of the Saudi national vision. Additionally, they showed that the national vision 2030 is implemented in education more than in healthcare.

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Arabic words clustering by using K-means algorithm

Cited by 10 publications

References 5 publications

Document Clustering in the Age of Big Data: Incorporating Semantic Information for Improved Results

Document Clustering in the Age of Big Data: Incorporating Semantic Information for Improved Results

Automated Analysis of Comments on Press Articles in Websites (Articles Literature as Case Study)

Clustering Arabic Tweets for Saudi National Vision 2030

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