Sentiment analysis of a document using deep learning approach and decision trees

Zharmagambetov, Arman; Pak, Alexandr

doi:10.1109/icecco.2015.7416902

Cited by 41 publications

(16 citation statements)

References 3 publications

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“…Consequently, to cope with the new trend of data, where efficient approaches for sentiment analysis are needed, researchers realized that the DL approaches give incredible results as affirmed by [5,47,48], and hence they are adopted for sentiment analysis.…”

Section: Traditional Approaches For Sentiment Analysismentioning

confidence: 99%

“…Researchers have tried to perform sentiment analysis by not only relying on the use of plain DL approaches but also integrating with some other components. Correspondingly, Zharmagambetov et al [47] unified a deep RNN with decision tree (DeepRNN+DT) models. The Deep-RNN+DT model is built on top of word2vec pre-trained embedding and trained using the traditional RNN.…”

Section: Sentence Level Sentiment Classificationmentioning

confidence: 99%

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Sentiment analysis using deep learning approaches: an overview

Habimana

et al. 2019

Sci. China Inf. Sci.

147

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Section: Traditional Approaches For Sentiment Analysismentioning

confidence: 99%

Section: Sentence Level Sentiment Classificationmentioning

confidence: 99%

Sentiment analysis using deep learning approaches: an overview

Habimana

et al. 2019

Sci. China Inf. Sci.

147

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show abstract

“…A growing number of data mining techniques have been applied to text classification problem, including the Bayes probabilistic approach [38], [39], decision trees [40], [41], neural networks [42], [43], support vector machines (SVM) [44], [45], [46], and k-nearest neighbor [47], [48]. In this preliminary study, three conventional classification algorithms [49]: nearest neighbor (k-NN), SVM, and naïve bayes classifiers are implemented for the labeling task.…”

Section: Classification Modelmentioning

confidence: 99%

Comparative Analysis of Text Classification Algorithms for Automated Labelling of Quranic Verses.

Adeleke¹,

Samsudin²,

Nawi³

2017

International Journal on Advanced Science, Engineering and Information Technology

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The ultimate goal of labelling a Quranic verse is to determine its corresponding theme. However, the existing Quranic verse labelling approach is primarily depending on the availability of Quranic scholars who have expertise in Arabic language and Tafseer. In this paper, we propose to automate the labelling task of the Quranic verse using text classification algorithms. We applied three text classification algorithms namely, k-Nearest Neighbour, Support Vector Machine, and Naïve Bayes in automating the labelling procedure. In our experiment with the classification algorithms English translation of the verses are presented as features. The English translation of the verses are then classified as "Shahadah" (the first pillar of Islam) or "Pray" (the second pillar of Islam). It is found that all of the text classification algorithms are capable to achieve more than 70% accuracy in labelling the Quranic verses.

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“…Some of the existing works as found in literatures include: text classification applications on the Holy Quran [1,3,[11][12][13]; ontology-based applications [14][15][16][17]; digitized Holy Quran applications [18][19][20][21][22]. Furthermore, conventional among machine learning algorithms often implemented in ML tasks include: naïve bayes (NB) [4], decision trees (J48) [23], neural networks [24], support vector machines (SVM) [25], and k-nearest neighbour (k-NN) [26].…”

Section: Introductionmentioning

confidence: 99%

Automating quranic verses labeling using machine learning approach

Adeleke

Samsudin

Khalid

2019

IJEECS

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Classification of Quranic verses into predefined categories is an essential task in Quranic studies. However, in recent times, with the advancement in information technology and machine learning, several classification algorithms have been developed for the purpose of text classification tasks. Automated text classification (ATC) is a well-known technique in machine learning. It is the task of developing models that could be trained to automatically assign to each text instances a known label from a predefined state. In this paper, four conventional ML classifiers: support vector machine (SVM), naïve bayes (NB), decision trees (J48), nearest neighbor (k-NN), are used in classifying selected Quranic verses into three predefined class labels: faith (iman), worship (ibadah), etiquettes (akhlak). The Quranic data comprises of verses in chapter two (al-Baqara) of the holy scripture. In the results, the classifiers achieved above 80% accuracy score with naïve bayes (NB) algorithm recording the overall highest scores of 93.9% accuracy and 0.964 AUC.

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Sentiment analysis of a document using deep learning approach and decision trees

Cited by 41 publications

References 3 publications

Sentiment analysis using deep learning approaches: an overview

Sentiment analysis using deep learning approaches: an overview

Comparative Analysis of Text Classification Algorithms for Automated Labelling of Quranic Verses.

Automating quranic verses labeling using machine learning approach

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