Cluster analysis of urdu tweets

Nasim, Zarmeen; Haider, Sajjad

doi:10.1016/j.jksuci.2020.08.008

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…The normalized pointwise mutual information is used to compute the co-occurrence counts. Nasim et al 30 used Urdu tweets to do an experimental test of clustering techniques. Tweets are used to retrieve functions including sentence and word embedding, TF-IDF aspects, and clustering is performed using three main techniques: Affinity Propagation, K-Means, and Bisecting K-Means.…”

Section: Related Workmentioning

confidence: 99%

Investigating topic modeling techniques through evaluation of topics discovered in short texts data across diverse domains

Muthusami,

Mani Kandan,

Saritha

et al. 2024

Sci Rep

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The online channel has affected many facets of an individual's identity, commercial, social policy, and culture, among others. It implies that discovering the topics on which these brief writings are focused, as well as examining the qualities of these short texts is critical. Another key issue that has been identified is the evaluation of newly discovered topics in terms of topic quality, which includes topic separation and coherence. A topic modeling method has been shown to be an outstanding aid in the linguistic interpretation of quite tiny texts. Based on the underlying strategy, topic models are divided into two categories: probabilistic methods and non-probabilistic methods. In this research, short texts are analyzed using topic models, including latent Dirichlet allocation (LDA) for probabilistic topic modeling and non-negative matrix factorization (NMF) for non-probabilistic topic modeling. A novel approach for topic evaluation is used, such as clustering methods and silhouette analysis on both models, to investigate performance in terms of quality. The experiment results indicate that the proposed evaluation method outperforms on both LDA and NMF.

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Section: Related Workmentioning

confidence: 99%

Investigating topic modeling techniques through evaluation of topics discovered in short texts data across diverse domains

Muthusami,

Mani Kandan,

Saritha

et al. 2024

Sci Rep

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“…However, as the authors previously indicated, Steinbach et al made these judgments only based on document collections (i.e., IR dataset). Nasim and Haider (2020) conducted an experimental investigation to determine the ideal clustering technique for Bahasa Indonesia. They tested three clustering algorithms, namely, k-means, k-means++, and AHC.…”

Section: Related Workmentioning

confidence: 99%

Neighboring-Aware Hierarchical Clustering

Amer,

Al-Razgan,

Abdalla

et al. 2024

International Journal on Semantic Web and Information Systems

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In this work, a simple yet robust neighboring-aware hierarchical-based clustering approach (NHC) is developed. NHC employs its dynamic technique to take into account the surroundings of each point when clustering, making it extremely competitive. NHC offers a straightforward design and reliable clustering. It comprises two key techniques, namely, neighboring- aware and filtering and merging. While the proposed neighboring-aware technique helps find the most coherent clusters, filtering and merging help reach the desired number of clusters during the clustering process. The NHC's performance, which includes all evaluation metrics and run time, has been thoroughly tested against nine clustering rivals using four similarity measures on several real-world numerical and textual datasets. The evaluation is done in two phases. First, we compare NHC to three common clustering methods and show its efficacy through empirical analysis. Second, a comparison with six relevant, contemporary competitors highlights NHC's extremely competitive performance.

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“…Word2vec, being a form of word embedding, has proven to be exceptionally proficient in producing numeric representations for textual data. Transforming textual content into numerical representations facilitates subsequent analysis using algorithms such as cluster analysis [25]. Based on this functionality, Word2vec has been used in the domain of fault diagnosis for analyzing text from maintenance records [26].…”

Section: Word2vecmentioning

confidence: 99%

“…Its corresponding vector representation is denoted as v w j ∈ R N , which is a row vector of the output weights W V×N . forming textual content into numerical representations facilitates subsequent analysis using algorithms such as cluster analysis [25]. Based on this functionality, Word2vec has been used in the domain of fault diagnosis for analyzing text from maintenance records [26].…”

Section: Word2vecmentioning

confidence: 99%

Clustering Analysis of Wind Turbine Alarm Sequences Based on Domain Knowledge-Fused Word2vec

Wei,

Wang,

Liu

et al. 2023

Applied Sciences

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The alarm data contain abundant fault information related to almost all components of the wind turbine. Reasonable analysis and utilization of alarm data can assist wind farm maintenance personnel in quickly identifying the types of turbine faults, reducing operation and maintenance costs. This paper proposes a clustering analysis method that groups similar alarm sequences with the same fault type. Firstly, the alarm data are preprocessed, where alarm sequences are segmented, and redundant alarms are removed. Then, a domain knowledge-fused Word2vec (DK-Wrod2vec) method is introduced to transform non-numeric alarm codes into numeric vector representations. Finally, new distance metrics are incorporated into the K-means clustering algorithm to improve clustering performance. The performance of the proposed clustering method is assessed by applying it to labeled alarm sequences. The results demonstrate that the clustering performance is the best when using DK-Word2vec and the word rotator’s distance compared with other methods. Additionally, with the optimal parameter combination, the fault types of unlabeled alarm sequences are also analyzed.

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Cluster analysis of urdu tweets

Cited by 10 publications

References 20 publications

Investigating topic modeling techniques through evaluation of topics discovered in short texts data across diverse domains

Investigating topic modeling techniques through evaluation of topics discovered in short texts data across diverse domains

Neighboring-Aware Hierarchical Clustering

Clustering Analysis of Wind Turbine Alarm Sequences Based on Domain Knowledge-Fused Word2vec

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