English poems categorization using text mining  and rough set theory

Alsaidi, Saif Ali; Sadeq, Ahmed Tariq; Abdullah, Hasanen S.

doi:10.11591/eei.v9i4.1898

Cited by 9 publications

(8 citation statements)

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“…In accordance with Figure 2, the experiment from all transformer summarization models that we build first performed preprocessing on the text. The use of preprocessing is to reduce less relevant features, and the amount of memory needed to carry out the training process [28]. Then after preprocessing, we perform all models transformer-based architecture with encoder and decoder that can be done several times repeatedly and make a comparison of layer modifications based on scoring.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Summarization of COVID-19 news documents deep learning-based using transformer architecture

2021

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Facing the news on the internet about the spreading of Corona virus disease 2019 (COVID-19) is challenging because it is required a long time to get valuable information from the news. Deep learning has a significant impact on NLP research. However, the deep learning models used in several studies, especially in document summary, still have a deficiency. For example, the maximum output of long text provides incorrectly. The other results are redundant, or the characters repeatedly appeared so that the resulting sentences were less organized, and the recall value obtained was low. This study aims to summarize using a deep learning model implemented to COVID-19 news documents. We proposed transformer as base language models with architectural modification as the basis for designing the model to improve results significantly in document summarization. We make a transformer-based architecture model with encoder and decoder that can be done several times repeatedly and make a comparison of layer modifications based on scoring. From the resulting experiment used, ROUGE-1 and ROUGE-2 show the good performance for the proposed model with scores 0.58 and 0.42, respectively, with a training time of 11438 seconds. The model proposed was evidently effective in improving result performance in abstractive document summarization.

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Section: Experiments and Discussionmentioning

confidence: 99%

Summarization of COVID-19 news documents deep learning-based using transformer architecture

2021

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“…English: Saif et al [17] proposed use of TF_IDF and the rough set theory (RST) algorithm to build a classification model for English poetry. The accuracy reached 90%.…”

Section: Poetry Classification In Other Languagesmentioning

confidence: 99%

“…These studies have provided technical support for further research on poetry categorization. Over the past few years, poetry categorization has been conducted in many different languages, including Spanish, Marathi [15], Portuguese [16], English [17], Ottoman [18], Chinese [19], Punjabi [20], and Gujarati [21]. However, poetry style classification remains an unexplored area.…”

Section: Introductionmentioning

confidence: 99%

An Artificial-Intelligence-Driven Spanish Poetry Classification Framework

Deng,

Wang,

Wang

et al. 2023

BDCC

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Spain possesses a vast number of poems. Most have features that mean they present significantly different styles. A superficial reading of these poems may confuse readers due to their complexity. Therefore, it is of vital importance to classify the style of the poems in advance. Currently, poetry classification studies are mostly carried out manually, which creates extremely high requirements for the professional quality of classifiers and consumes a large amount of time. Furthermore, the objectivity of the classification cannot be guaranteed because of the influence of the classifier’s subjectivity. To solve these problems, a Spanish poetry classification framework was designed using artificial intelligence technology, which improves the accuracy, efficiency, and objectivity of classification. First, an artificial-intelligence-driven Spanish poetry classification framework is described in detail, and is illustrated by a framework diagram to clearly represent each step in the process. The framework includes many algorithms and models, such as the Term Frequency–Inverse Document Frequency (TF_IDF), Bagging, Support Vector Machines (SVMs), Adaptive Boosting (AdaBoost), logistic regression (LR), Gradient Boosting Decision Trees (GBDT), LightGBM (LGB), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The roles of each algorithm in the framework are clearly defined. Finally, experiments were performed for model selection, comparing the results of these algorithms.The Bagging model stood out for its high accuracy, and the experimental results showed that the proposed framework can help researchers carry out poetry research work more efficiently, accurately, and objectively.

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“…The next research was carried out by applying a neural network model that had optimized parameter values and resulted in a classification accuracy rate of 88.99% [22]. Based on a model with a known level of accuracy, this study develops and builds a sentiment analysis-based application to classify hotel service ratings based on customer reviews using Indonesian text, slightly different from [23]. The basic contribution of this research is that it offers an intelligent sentiment analysis application SentiHotel which is developed by integrating a neural network model that has been optimized for parameter values using a genetic algorithm.…”

Section: Bulletin Of Electr Eng and Infmentioning

confidence: 99%

SentiHotel: a sentiment analysis application of hotel services using an optimized neural network

et al. 2021

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An assessed hotel service is necessary for tourists and everyone who is traveling, however currently it is still difficult to find recommended hotel information. The solution provided in this research is to propose a smart application that has been developed by implementing machine learning in it. The purpose is to build a sentiment review smart application by applying the sentiment analysis hybrid model of the best neural network (NN) algorithm model that has been optimized using genetic algorithms. To get the right model, the research method was carried out with experiments starting from the initial stages of conducting data preprocessing, tokenization, weighting, modeling experiments, and conducting the system evaluation stage to determine the success of the proposed model. The progress of the application development system is by using the prototyping model. SentiHotel is a sentiment application that was successfully built to provide a solution for tourists in assessing a hotel service. The software validation test is carried out using the blackbox method and the results show that the SentHotel application is in accordance with the expected result; all system functions can run properly.

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English poems categorization using text mining and rough set theory

Cited by 9 publications

References 0 publications

Summarization of COVID-19 news documents deep learning-based using transformer architecture

Summarization of COVID-19 news documents deep learning-based using transformer architecture

An Artificial-Intelligence-Driven Spanish Poetry Classification Framework

SentiHotel: a sentiment analysis application of hotel services using an optimized neural network

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