Weirdness Coefficient as a Feature Selection Method for Arabic Special Domain Text Classification

Al-Thubaity, Abdulmohsen; Alanazi, Adwan; Hazzaa, Itisam; Al-Tuwaijri, Haya

doi:10.1109/ialp.2012.64

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…TC algorithms require that text features are formatted before they can be interpreted by the specified classifier, this process is also referred to as term weighting because each term is entered together with a weight value. Included papers show the most used technique is the Term Frequency-Inverse Document Frequency (TF-IDF) as in [27,32,37,40,43,45,48,51,53,55,57,58,[60][61][62]67]. It is a statistical method to indicate the significance of a word within a given corpus.…”

Section: E Feature Reresentation (Term Weighting)mentioning

confidence: 99%

“…This utilization of the technique is justified assuming the authors wanted to weight terms while considering its significance across all documents rather than a single one. Although, in [58] a simpler but more limited method has also been used to conclude a Boolean value of zero or one, a term can be described to be either important or not important. Whilst in TF-IDF, for a given term, a bigger TF-IDF value indicates a more frequent word.…”

Section: E Feature Reresentation (Term Weighting)mentioning

confidence: 99%

See 1 more Smart Citation

Arabic text classification methods: Systematic literature review of primary studies

Alabbas

Al-Khateeb

Mansour

2016

2016 4th IEEE International Colloquium on Information Science and Technology (CiSt)

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Section: E Feature Reresentation (Term Weighting)mentioning

confidence: 99%

Section: E Feature Reresentation (Term Weighting)mentioning

confidence: 99%

Arabic text classification methods: Systematic literature review of primary studies

Alabbas

Al-Khateeb

Mansour

2016

2016 4th IEEE International Colloquium on Information Science and Technology (CiSt)

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“…Approaches such as term frequencies, term frequency-inverse document frequency (TF-IDF), weirdness coefficients, information gain and chi-squared are also used to extract quality terms and phrases. Al-Thubaity et al (2012) proposed a method to classify Arabic special texts using weirdness coefficients, and promising classification accuracy is obtained with respect to chi-squared. In this study, it was necessary to extract health-related phrases while evaluating the quality and coverage of websites.…”

Section: Quality Term Extraction From Contentmentioning

confidence: 99%

Quality assessment of web-based information on type 2 diabetes

Ölçer

Temizel

2021

OIR

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PurposeThis paper proposes a framework that automatically assesses content coverage and information quality of health websites for end-users.Design/methodology/approachThe study investigates the impact of textual and content-based features in predicting the quality of health-related texts. Content-based features were acquired using an evidence-based practice guideline in diabetes. A set of textual features inspired by professional health literacy guidelines and the features commonly used for assessing information quality in other domains were also used. In this study, 60 websites about type 2 diabetes were methodically selected for inclusion. Two general practitioners used DISCERN to assess each website in terms of its content coverage and quality.FindingsThe proposed framework outputs were compared with the experts' evaluation scores. The best accuracy was obtained as 88 and 92% with textual features and content-based features for coverage assessment respectively. When both types of features were used, the proposed framework achieved 90% accuracy. For information quality assessment, the content-based features resulted in a higher accuracy of 92% against 88% obtained using the textual features.Research limitations/implicationsThe experiments were conducted for websites about type 2 diabetes. As the whole process is costly and requires extensive expert human labelling, the study was carried out in a single domain. However, the methodology is generalizable to other health domains for which evidence-based practice guidelines are available.Practical implicationsFinding high-quality online health information is becoming increasingly difficult due to the high volume of information generated by non-experts in the area. The search engines fail to rank objective health websites higher within the search results. The proposed framework can aid search engine and information platform developers to implement better retrieval techniques, in turn, facilitating end-users' access to high-quality health information.Social implicationsErroneous, biased or partial health information is a serious problem for end-users who need access to objective information on their health problems. Such information may cause patients to stop their treatments provided by professionals. It might also have adverse financial implications by causing unnecessary expenditures on ineffective treatments. The ability to access high-quality health information has a positive effect on the health of both individuals and the whole society.Originality/valueThe paper demonstrates that automatic assessment of health websites is a domain-specific problem, which cannot be addressed with the general information quality assessment methodologies in the literature. Content coverage of health websites has also been studied in the health domain for the first time in the literature.

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“…The results revealed better performance in classification with PCA. In [24], the researchers performed text mining on an Arabic dataset from King Abdulaziz City for Science and Technology (KACST), covering five fields with about 2,243 texts from the Islamic topics of Feqh, Tafseer, Lughah, Aqeedah, and Hadeet. The researchers employed three schemas for representation-Boolean, LTC, and TF-IDF.…”

Section: Related Workmentioning

confidence: 99%

Arabic Text Classification using Feature-Reduction Techniques for Detecting Violence on Social Media

ALSaif¹,

Alotaibi²

2019

IJACSA

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With the current increase in the number of online users, there has been a concomitant increase in the amount of data shared online. Techniques for discovering knowledge from these data can provide us with valuable information when it comes to detecting different problems, including violence. Violence is one of the significant problems humanity has faced in recent years all over the world, and this is especially a problem in Arabic countries. To address this issue, this research focuses on detecting violence-related tweets to help in solving this problem. Text mining is an important technique that can be used to find and predict information from text. In this study, a text classification model is built for detecting violence in Arabic dialects on Twitter using different feature-reduction approaches. The experiment comprises bagging, K-nearest neighbors (KNN), and Bayesian boosting using different extraction features, namely, root-based stemming, light stemming, and n-grams. In addition, the study used the following feature-reduction techniques: support vector machine (SVM), Chi-squared (CHI), the Gini index, correlation, rules, information gain (IG), deviation, symmetrical uncertainty, and the IG ratio. The experiment showed that the bagging with tri-gram approach has the highest accuracy at 86.61%, and a combination of IG with SVM from reduction features registers an accuracy of 90.59%.

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Weirdness Coefficient as a Feature Selection Method for Arabic Special Domain Text Classification

Cited by 4 publications

References 12 publications

Arabic text classification methods: Systematic literature review of primary studies

Arabic text classification methods: Systematic literature review of primary studies

Quality assessment of web-based information on type 2 diabetes

Arabic Text Classification using Feature-Reduction Techniques for Detecting Violence on Social Media

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