Topic Classification from Text Using Decision Tree, K-NN and Multinomial Naïve Bayes

Rahman, Md. Ataur; Akter, Yeasmin Ara

doi:10.1109/icasert.2019.8934502

Cited by 16 publications

(8 citation statements)

References 4 publications

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“…94 The applied classification techniques range from shallow methods, such as Logistic Regression, 95 SVM, 96 and Naïve Bayes, 97 to more complex and resource-hungry deep neural networks, such as CNNs, 62,98 Hierarchical Attention Networks (HANs), 99 and the powerful transformer-based methods that started to dominate the landscape in recent years. 100,101 In the resourceful English language, the research community had the means to explore various topic classification techniques, from shallow methods, such as k-nearest neighbors, Multinomial Naïve Bayes and decision trees, 102 to deep forests 103 and Bayesian networks. 104 Non-English languages are targeted as well for topic classification.…”

Section: Text Classificationmentioning

confidence: 99%

“…In the resourceful English language, the research community had the means to explore various topic classification techniques, from shallow methods, such as k ‐nearest neighbors, Multinomial Naïve Bayes and decision trees, 102 to deep forests 103 and Bayesian networks 104 . Non‐English languages are targeted as well for topic classification.…”

Section: Related Workmentioning

confidence: 99%

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The unreasonable effectiveness of machine learning in Moldavian versus Romanian dialect identification

Găman

Ionescu

2021

Int J of Intelligent Sys

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Motivated by the seemingly high accuracy levels of machine learning (ML) models in Moldavian versus Romanian dialect identification and the increasing research interest on this topic, we provide a follow-up on the Moldavian versus Romanian Cross-Dialect Topic Identification (MRC) shared task of the VarDial 2019 evaluation campaign. The shared task included two subtask types: one that consisted in discriminating between the Moldavian and Romanian dialects and one that consisted in classifying documents by topic across the two dialects of Romanian. Participants achieved impressive scores, for example, the top model for Moldavian versus Romanian dialect identification obtained a macro-F 1 score of 0.895. We conduct a subjective evaluation by human annotators, showing that humans attain much lower accuracy rates compared with ML models. Hence, it remains unclear why the methods proposed by participants attain such high accuracy rates. Our goal is to understand (i) why the proposed methods work so well (by visualizing the discriminative features) and (ii) to what extent these methods can keep their high accuracy levels, for example, when we shorten the text samples to single sentences or when we use tweets at inference time. A secondary goal of our

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Section: Text Classificationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

The unreasonable effectiveness of machine learning in Moldavian versus Romanian dialect identification

Găman

Ionescu

2021

Int J of Intelligent Sys

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“…Metode Naïve Bayes merupakan pengembangan dari metode Bayes [10], di mana pada Naïve Bayes seluruh atribut yang ada dianggap independen atau tidak ada kaitan satu sama lain [11]. Metode Naïve Bayes banyak digunakan untuk keperluan pemrosesan teks dan dapat memberikan hasil yang cukup baik, salah satunya adalah untuk klasifikasi topik dengan hasil nilai akurasi sebesar 91.8% [12]. Naïve Bayes bekerja dengan menggunakan sistem probabilitas, di mana satu jenis data latih dapat dikategorikan terhadap beberapa kelas yang berbeda dengan nilai probabilitas untuk tiap kelas.…”

Section: Pendahuluanunclassified

Analisis Sentimen Berbasis Emoticon pada Komentar Instagram Bahasa Indonesia Menggunakan Naïve Bayes

Nugraha

2021

JuTISI

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The usage of social media growing rapidly, especially after the smartphone was invented. Because the number of social media users was quite a lot, companies prefer to promote their products through social media like Instagram. But, unlike TV or radio, social media is a two-way communication media, that makes users can respond directly to the content created by the company. Comments given by users have various types of sentiment, like positive or negative comments. In addition to using text, comments also often contain emoticons to support the message. This study tries to analyzing sentiment based on the usage of emoticons inside them using the Naïve Bayes algorithm. Based on the test results, the accuracy result is quite good, it is about 96.3% correct in sentiment classification.

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“…The classification is divided into specific topics because each Islamic consulting web has a different classification system [1,3]. However, the classification based on this topic is done manually by adopting the topic class provided by the Islamic consulting website [4]. Examples include topics of worship, prayer, faith, and so on.…”

Section: Introductionmentioning

confidence: 99%

“…The existence of many classification methods is a particular concern in this study [4]. Therefore, the author will focus on the classification method used in Naïve Bayes and using TF-IDF.…”

Section: Introductionmentioning

confidence: 99%

Topic Classification of Islamic Question and Answer Using Naïve Bayes and TF-IDF Method

Andini¹,

Murdiansyah

Lhaksmana³

2021

ComEngApp

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Information spread through the internet is widely used by people to find anything. One of the most searched information on the internet is information related to Islamic religious knowledge. However, the large amount of information available from various sources makes it difficult for people to find the correct information. Previous researchers have researched this topic, but the dataset used only comes from one source. Therefore, in this study, a classification system for Islamic question and answer topics was built using the Naïve Bayes and TF-IDF methods. This study using 1000 question and answer article data taken from Islamic consultation websites, namely rumahfiqih.com and islamqa.info. The multi-class classification uses five categories which are manually labeled using the category classes on the website. From several test scenarios in this study, the Naïve Bayes classification method using TF-IDF (n-gram level) with a maximum feature of 1000 at a data separation ratio of 70:30 produces the highest accuracy of 81%. The 81% accuracy value was also generated by the SVM classification method, but the difference was in the SVM the highest accuracy value using TF-IDF (word level). It is expected that in the subsequent research will be used more website sources and the use of other classification and feature extraction methods with more optimal value than previous research.

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Topic Classification from Text Using Decision Tree, K-NN and Multinomial Naïve Bayes

Cited by 16 publications

References 4 publications

The unreasonable effectiveness of machine learning in Moldavian versus Romanian dialect identification

The unreasonable effectiveness of machine learning in Moldavian versus Romanian dialect identification

Analisis Sentimen Berbasis Emoticon pada Komentar Instagram Bahasa Indonesia Menggunakan Naïve Bayes

Topic Classification of Islamic Question and Answer Using Naïve Bayes and TF-IDF Method

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