Impact of Text Pre-processing and Ensemble Learning on Arabic Sentiment Analysis

Oussous, Ahmed; Lahcen, Ayoub Ait; Belfkih, Samir

doi:10.1145/3320326.3320399

Cited by 23 publications

(11 citation statements)

References 20 publications

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“…Pedoman PICO sangat berguna dan memudahkan dalam merumuskan pertanyaan penelitian [20]. Tabel Dataset Peneliti Jumlah Twitter [33], [34], [35], [36], [23], [24], [37], [38], [39], [22], [40], [41], [42], [43], [44], [45], [46] 17 Berita berbahasa Arab [28], [47], [48], [49], [27], [25], [50], [51] 8…”

Section: Tahap Pembuatan Rencana Awalunclassified

“…Tabel 6. Analisis Metode Representasi Teks dalam Arabic Natural Language Processing Metode Untuk Representasi Teks Peneliti Jumlah TF-IDF [4], [9], [48], [30], [23], [24], [29], [53], [54], [7], [38], [55], [32], [56], [25], [31], [59], [1], [51], [60] 20 Word2Vec [34], [24], [49], [7], [57], [39], [41], [43], [45], [46] 10 AraVec [34], [35], [37], [40], [41], [42] 6 FastText [34], [47], [35], [57], [41], [42], [50], [46] 8 mBERT [27], [22], [41], [46] 4 AraBERT [36],…”

Section: Tahap Pembuatan Rencana Awalmentioning

confidence: 99%

“…Untuk lebih mempertimbangkan konteks kata dan makna kalimat dapat diatasi dengan dynamic atau contextual embedding yang mempelajari representasi kalimat secara universal [27]. Contextual embedding yang pertama kali diusulkan adalah Embeddings from Language Model (ELMO) [68] [33], [34], [47], [48], [30], [23], [24], [54], [38], [27], [56], [31], [57], [39], [1], [42], [51], [46], [9] 20…”

Section: Tahap Pembuatan Rencana Awalunclassified

“…Naïve Bayes [33], [47], [48], [30], [23], [24], [38], [55], [56], [58], [39], [1], [51], [60], [46] 15…”

Section: Tahap Pembuatan Rencana Awalmentioning

confidence: 99%

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Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

Ariyanto

fatichah²,

Arifin³

2021

RESTI

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Hadith is the second source of reference for Islamic law after the Qur'an, which explains the sentences in the Qur'an which are still global by referring to the provisions of the Prophet Muhammad SAW. Classification of text documents can also be used to overcome the problem of interrelation between the Qur'an and hadith. The problem of interrelation between books of hadith needs to be done because some hadiths in certain hadith books have the same meaning as other hadith books. This study aims to analyze the development of text representation and classification methods suitable to overcome similarity meaning problems in detecting interrelationships between hadith books. The research method used is Systematic Literature Review (SLR) sourced from Google Scholar, Science Direct, and IEEE. There are 42 pieces of literature that have been studied successfully. The results showed that contextual embedding as the newest text representation method considered word context and sentence meaning better than static embedding. As a classification method, the ensemble method has better performance in classifying text documents than using only a single classifier model. Thus, future research can consider using a combination of contextual embedding and ensemble methods to detect interrelationships between books of hadith.

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Section: Tahap Pembuatan Rencana Awalunclassified

Section: Tahap Pembuatan Rencana Awalmentioning

confidence: 99%

Section: Tahap Pembuatan Rencana Awalunclassified

“…Naïve Bayes [33], [47], [48], [30], [23], [24], [38], [55], [56], [58], [39], [1], [51], [60], [46] 15…”

Section: Tahap Pembuatan Rencana Awalmentioning

confidence: 99%

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Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

Ariyanto

fatichah²,

Arifin³

2021

RESTI

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“…Ref [55] studied the impact of stemming on sentiment analysis using SVM, NB, and Maximum Entropy. For the sake of comparison, the authors used different stemmers [11], [16], [30], [42], [56].…”

Section: Related Workmentioning

confidence: 99%

Impact of Stemming and Word Embedding on Deep Learning-Based Arabic Text Categorization

Almuzaini

Azmi

2020

IEEE Access

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Document classification is a classical problem in information retrieval, and plays an important role in a variety of applications. Automatic document classification can be defined as content-based assignment of one or more predefined categories to documents. Many algorithms have been proposed and implemented to solve this problem in general, however, classifying Arabic documents is lagging behind similar works in other languages. In this paper, we present seven deep learning-based algorithms to classify the Arabic documents. These are: Convolutional Neural Network (CNN), CNN-LSTM (LSTM = Long Short-Term Memory), CNN-GRU (GRU = Gated Recurrent Units), BiLSTM (Bidirectional LSTM), BiGRU, Att-LSTM (Attention-based LSTM), and Att-GRU. And for word representation, we applied the word embedding technique (Word2Vec). We tested our approach on two large datasets-with six and eight categories-using ten-fold cross-validation. Our objective was to study how the classification is affected by the stemming strategies and word embedding. First, we looked into the effects of different stemming algorithms on the document classification with different deep learning models. We experimented with eleven different stemming algorithms, broadly falling into: root-based and stem-based, and no stemming. We performed ANOVA test on the classification results using the different stemmers, which helps assure if the results are significant. The results of our study indicate that stem-based algorithms perform slightly better compared to root-based algorithms. Among the deep learning models, the Attention mechanism and the Bidirectional learning gave outstanding performance with Arabic text categorization. Our best performance is F -score = 97.96%, achieved using the Att-GRU model with stem-based algorithm. Next, we looked into different controlling parameters for word embedding. For Word2Vec, both skip-gram and bag-of-words (CBOW) perform well with either stemming strategies. However, when using a stem-based algorithm, skipgram achieves good results with a vector of smaller dimension, while CBOW requires a larger dimension vector to achieve a similar performance.

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Political Articles Categorization Based on Different Naïve Bayes Models

Hamed

Sadiq

Abbas

2020

Communications in Computer and Information Science

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Impact of Text Pre-processing and Ensemble Learning on Arabic Sentiment Analysis

Cited by 23 publications

References 20 publications

Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

Impact of Stemming and Word Embedding on Deep Learning-Based Arabic Text Categorization

Political Articles Categorization Based on Different Naïve Bayes Models

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