Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis

Abualigah, Laith; Gandomi, Amir H.; Elaziz, Mohamed Abd; Hussien, Abdelazim G.; Khasawneh, Ahmad M.; Alshinwan, Mohammad; Houssein, Essam H.

doi:10.3390/a13120345

Cited by 70 publications

(29 citation statements)

References 97 publications

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“…For instance, the score for each class is calculated based on averaging the corresponding predicted AUCs for each class. The metric is the standard evaluation method used in the Kaggle competition which can be define as in Equation (1).…”

Section: Model Fine-tuning For Toxic Comments Classificationmentioning

confidence: 99%

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Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit

Fan

Dahou

et al. 2021

Electronics

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Social media has become an essential facet of modern society, wherein people share their opinions on a wide variety of topics. Social media is quickly becoming indispensable for a majority of people, and many cases of social media addiction have been documented. Social media platforms such as Twitter have demonstrated over the years the value they provide, such as connecting people from all over the world with different backgrounds. However, they have also shown harmful side effects that can have serious consequences. One such harmful side effect of social media is the immense toxicity that can be found in various discussions. The word toxic has become synonymous with online hate speech, internet trolling, and sometimes outrage culture. In this study, we build an efficient model to detect and classify toxicity in social media from user-generated content using the Bidirectional Encoder Representations from Transformers (BERT). The BERT pre-trained model and three of its variants has been fine-tuned on a well-known labeled toxic comment dataset, Kaggle public dataset (Toxic Comment Classification Challenge). Moreover, we test the proposed models with two datasets collected from Twitter from two different periods to detect toxicity in user-generated content (tweets) using hashtages belonging to the UK Brexit. The results showed that the proposed model can efficiently classify and analyze toxic tweets.

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Section: Model Fine-tuning For Toxic Comments Classificationmentioning

confidence: 99%

“…Social media has many positive aspects, one of which is the sense of community it provides to people [1]. Social media allows people to do in a day what would usually take a lifetime to achieve.…”

Section: Introductionmentioning

confidence: 99%

Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit

Fan

Dahou

et al. 2021

Electronics

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“…Judith and Jayakumari [ 23 ] proposed a hybrid algorithm that includes K-means and PSO to solve the distributed document clustering problem. Abualigah et al [ 24 ] compared a few nature-inspired optimization algorithms in text document clustering and the result showed that, according to the accuracy measure and F-measure, GWO is the best performing algorithm and GA is the least performing algorithm. Rashaideh et al [ 25 ] studied the GWO algorithm in a text document clustering problem and their result shows that GWO performs better than the

-hill climbing and hill-climbing algorithms.…”

Section: Related Workmentioning

confidence: 99%

Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks

Selvaraj

Choi

2021

Sensors

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Text document clustering refers to the unsupervised classification of textual documents into clusters based on content similarity and can be applied in applications such as search optimization and extracting hidden information from data generated by IoT sensors. Swarm intelligence (SI) algorithms use stochastic and heuristic principles that include simple and unintelligent individuals that follow some simple rules to accomplish very complex tasks. By mapping features of problems to parameters of SI algorithms, SI algorithms can achieve solutions in a flexible, robust, decentralized, and self-organized manner. Compared to traditional clustering algorithms, these solving mechanisms make swarm algorithms suitable for resolving complex document clustering problems. However, each SI algorithm shows a different performance based on its own strengths and weaknesses. In this paper, to find the best performing SI algorithm in text document clustering, we performed a comparative study for the PSO, bat, grey wolf optimization (GWO), and K-means algorithms using six data sets of various sizes, which were created from BBC Sport news and 20 newsgroups. Based on our experimental results, we discuss the features of a document clustering problem with the nature of SI algorithms and conclude that the PSO and GWO SI algorithms are better than K-means, and among those algorithms, the PSO performs best in terms of finding the optimal solution.

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“…Clustering is a technique of finding groups of similar data based on their attributes. Effective clustering of the highdimensional dataset is an important research issue in the field of data mining (L. Abualigah et al, 2020;L. M. Q. Abualigah, 2019).…”

Section: Introductionmentioning

confidence: 99%

A meta-heuristic density-based subspace clustering algorithm for high-dimensional data

2021

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Subspace clustering is one of the efficient techniques for determining the clusters in different subsets of dimensions. Ideally, these techniques should find all possible non-redundant clusters in which the data point participates. Unfortunately, existing hard subspace clustering algorithms fail to satisfy this property. Additionally, with the increase in dimensions of data, classical subspace algorithms become inefficient. This work presents a new density-based subspace clustering algorithm (S_FAD) to overcome the drawbacks of classical algorithms. S_FAD is based on a bottom-up approach and finds subspace clusters of varied density using different parameters of the DBSCAN algorithm. The algorithm optimizes parameters of the DBCAN algorithm through a hybrid meta-heuristic algorithm and uses hashing concepts to discover all non-redundant subspace clusters. The efficacy of S_FAD is evaluated against various existing subspace clustering algorithms on artificial and real datasets in terms of F_Score and rand_index. Performance is assessed based on three parameters: average ranking, SRR ranking, and scalability on varied dimensions. Statistical analysis is performed through the Wilcoxon signed-rank test. Results reveal that S_FAD performs considerably better on the majority of the datasets and scales well up to 6400 dimensions on the actual dataset.

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Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis

Cited by 70 publications

References 97 publications

Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit

Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit

Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks

A meta-heuristic density-based subspace clustering algorithm for high-dimensional data

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