Class dependent feature scaling method using naive Bayes classifier for text datamining

Youn, Eunseog; Jeong, Myong K.

doi:10.1016/j.patrec.2008.11.013

Cited by 75 publications

(37 citation statements)

References 18 publications

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“…He concluded that this classification provides a standard of ideal decision making. Bayesian classification, not only widely used 10 , but also popularly used in text classification 19 . This is due to its non-requirement in any adjustment of parameters.…”

Section: Bayesmentioning

confidence: 99%

Machine Learning Classifiers: Evaluation of the Performance in Online Reviews

Pak¹,

Teh²

2016

Indian Journal of Science and Technology

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Objectives: This paper aims to evaluate the performance of the machine learning classifiers and identify the most suitable classifier for classifying sentiment value. The term "sentiment value" in this study is referring to the polarity (positive, negative or neutral) of the text. Methods/Analysis: This work applies machine learning classifiers from WEKA (Waikato Environment for Knowledge Analysis) toolkit in order to perform their evaluation. WEKA toolkit is a great set of tools for data mining and classification. The performance of the machine learning classifiers was measured by examining overall accuracy, recall, precision, kappa statistic and applying few visualization techniques. Finally, the analysis is applied to find the most suitable classifier for classifying sentiment value. Findings: Results show that two classifiers from Rules and Trees categories of classifiers perform equally best comparing to the other classifiers from categories, such as Bayes, Functions, Lazy and Meta. Novelty /Improvement: This paper explores the performance of machine learning classifiers in sentiment value classification in the online reviews. Data used is never been used before to explore the performance of machine learning classifiers.

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Section: Bayesmentioning

confidence: 99%

Machine Learning Classifiers: Evaluation of the Performance in Online Reviews

Pak¹,

Teh²

2016

Indian Journal of Science and Technology

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“…The larger the value of a feature information gain is, the more significant for categorization the feature is. The information gain of a feature t k toward a category c i can be defined as follows: (1) Where t k is one of the all features and c i is one of the all classes; P(c) is the fraction of the documents in category c over the total number of documents;P(t,c) is the fraction of documents in the category c that contain the word t over the total number of documents;P(t) is the fraction of the documents containing the term t over the total number of documents [22] …”

Section: Information Gainmentioning

confidence: 99%

“…The Naïve Bayes classifier, which is one of the most extensively used machine learning methods, is popular in text categorization. It successful applications to text document datasets have been shown in many literatures [22]. the Naïve Bayes classifier is simple to be performed and no parameters need to be adjusted [22].…”

Section: Classifiersmentioning

confidence: 99%

A Novel Feature Selection Based Gravitation for Text Categorization

Yang¹,

Liu²,

Qu³

2016

IJDTA

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“…Keyphrase extraction also provides useful resources for text clustering [8], text classification [35], and document summarization [32]. There are two main types of studies on keyphrase extraction, i.e., supervised and unsupervised.…”

Section: Introductionmentioning

confidence: 99%

Keyphrase Extraction Using Knowledge Graphs

Wei

Zheng

et al. 2017

Data Sci. Eng.

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Extracting keyphrases from documents automatically is an important and interesting task since keyphrases provide a quick summarization for documents. Although lots of efforts have been made on keyphrase extraction, most of the existing methods (the co-occurrence-based methods and the statistic-based methods) do not take semantics into full consideration. The co-occurrence-based methods heavily depend on the co-occurrence relations between two words in the input document, which may ignore many semantic relations. The statistic-based methods exploit the external text corpus to enrich the document, which introduce more unrelated relations inevitably. In this paper, we propose a novel approach to extract keyphrases using knowledge graphs, based on which we could detect the latent relations of two keyterms (i.e., noun words and named entities) without introducing many noises. Extensive experiments over real data show that our method outperforms the state-of-the-art methods including the graph-based co-occurrence methods and statistic-based clustering methods.

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Class dependent feature scaling method using naive Bayes classifier for text datamining

Cited by 75 publications

References 18 publications

Machine Learning Classifiers: Evaluation of the Performance in Online Reviews

Machine Learning Classifiers: Evaluation of the Performance in Online Reviews

A Novel Feature Selection Based Gravitation for Text Categorization

Keyphrase Extraction Using Knowledge Graphs

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