The classification of the modern arabic poetry using machine learning

Ahmed, Munef Abdullah; Hasan, Raed Abdulkareem; Ali, Ahmed Hussein; Mohammed, Mostafa Abdulghafoor

doi:10.12928/telkomnika.v17i5.12646

Cited by 24 publications

(11 citation statements)

References 21 publications

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“…Hence it is necessary to know its sentiment classification in response to the hoax news. Some methods that are widely used to classify text and conduct sentiment analysis are Naïve Bayes [15][16][17][18][19], Support Vector Machine [20][21][22], and KNN [23,24]. In this research, Naïve Bayes method was chosen to carry out classification and sentiment analysis on Hoax news.…”

Section:  Issn: 1693-6930mentioning

confidence: 99%

Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization

et al. 2020

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Currently, the spread of hoax news has increased significantly, especially on social media networks. Hoax news is very dangerous and can provoke readers. So, this requires special handling. This research proposed a hoax news detection system using searching, snippet and cosine similarity methods to classify hoax news. This method is proposed because the searching method does not require training data, so it is practical to use and always up to date. In addition, one of the drawbacks of the existing approaches is they are not equipped with a sentiment analysis feature. In our system, sentiment analysis is carried out after hoax news is detected. The goal is to extract the true hidden sentiment inside hoax whether positive sentiment or negative sentiment. In the process of sentiment analysis, the Naïve Bayes (NB) method was used which was optimized using the Particle Swarm Optimization (PSO) method. Based on the results of experiment on 30 hoax news samples that are widely spread on social media networks, the average of hoax news detection reaches 77% of accuracy, where each news is correctly identified as a hoax in the range between 66% and 91% of accuracy. In addition, the proposed sentiment analysis method proved to has a better performance than the previous analysis sentiment method.

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Section:  Issn: 1693-6930mentioning

confidence: 99%

Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization

et al. 2020

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“…However, the studies concerning the categorization of the texts in Arabic are limited [8]. Several methods and systems have been developed for classifying English texts which provide brilliant outcomes with high precision, thanks to the nature of the words, language and the letters in the English language [9]. Arabic Natural Language Processing (NLP) is a challenging process due to the challenging morphology and structure of the language.…”

Section: Introductionmentioning

confidence: 99%

Automated Arabic Text Classification Using Hyperparameter Tuned Hybrid Deep Learning Model

Al-onazi¹,

Alotaib²,

Alshahrani³

et al. 2023

Computers, Materials &Amp; Continua

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The text classification process has been extensively investigated in various languages, especially English. Text classification models are vital in several Natural Language Processing (NLP) applications. The Arabic language has a lot of significance. For instance, it is the fourth mostly-used language on the internet and the sixth official language of the United Nations. However, there are few studies on the text classification process in Arabic.A few text classification studies have been published earlier in the Arabic language. In general, researchers face two challenges in the Arabic text classification process: low accuracy and high dimensionality of the features. In this study, an Automated Arabic Text Classification using Hyperparameter Tuned Hybrid Deep Learning (AATC-HTHDL) model is proposed. The major goal of the proposed AATC-HTHDL method is to identify different class labels for the Arabic text. The first step in the proposed model is to pre-process the input data to transform it into a useful format. The Term Frequency-Inverse Document Frequency (TF-IDF) model is applied to extract the feature vectors. Next, the Convolutional Neural Network with Recurrent Neural Network (CRNN) model is utilized to classify the Arabic text. In the final stage, the Crow Search Algorithm (CSA) is applied to fine-tune the CRNN model's hyperparameters, showing the work's novelty. The proposed AATC-HTHDL model was experimentally validated under different parameters and 5448 CMC, 2023, vol.74, no.3 the outcomes established the supremacy of the proposed AATC-HTHDL model over other approaches.

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“…Its morphology serves a significant role. Further, the words are frequently built in a complicated manner [6,7] and might contain agglutinative words, drop features and affixes. The Arabic language's characteristics and complexities make its opinion mining process a highly challenging one [8].…”

Section: Introductionmentioning

confidence: 99%

Political Optimizer with Probabilistic Neural Network-Based Arabic Comparative Opinion Mining

Mohsen¹,

Al-onazi²,

Nour³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Opinion Mining (OM) studies in Arabic are limited though it is one of the most extensively-spoken languages worldwide. Though the interest in OM studies in the Arabic language is growing among researchers, it needs a vast number of investigations due to the unique morphological principles of the language. Arabic OM studies experience multiple challenges owing to the poor existence of language sources and Arabic-specific linguistic features. The comparative OM studies in the English language are wide and novel. But, comparative OM studies in the Arabic language are yet to be established and are still in a nascent stage. The unique features of the Arabic language make it essential to expand the studies regarding the Arabic text. It contains unique features such as diacritics, elongation, inflection and word length. The current study proposes a Political Optimizer with Probabilistic Neural Networkbased Comparative Opinion Mining (POPNN-COM) model for the Arabic text. The proposed POPNN-COM model aims to recognize comparative and non-comparative texts in Arabic in the context of social media. Initially, the POPNN-COM model involves different levels of data pre-processing to transform the input data into a useful format. Then, the pre-processed data is fed into the PNN model for classification and recognition of the data under different class labels. At last, the PO algorithm is employed for finetuning the parameters involved in this model to achieve enhanced results. The proposed POPNN-COM model was experimentally validated using two standard datasets, and the outcomes established the promising performance of the proposed POPNN-COM method over other recent approaches.

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The classification of the modern arabic poetry using machine learning

Cited by 24 publications

References 21 publications

Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization

Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization

Automated Arabic Text Classification Using Hyperparameter Tuned Hybrid Deep Learning Model

Political Optimizer with Probabilistic Neural Network-Based Arabic Comparative Opinion Mining

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