Classifying Positive or Negative Text Using Features Based on Opinion Words and Term Frequency - Inverse Document Frequency

Examination is one of the common ways to evaluate the students’ cognitive levels in higher education institutions. Exam questions are labeled manually by educators in accordance with Bloom’s taxonomy cognitive domain. To ease the burden of the educators, several past research works have proposed the automated question classification based on Bloom’s taxonomy using the machine learning technique. Feature selection, feature extraction and term weighting are common ways to improve the accuracy of question classification. Commonly used term weighting method in the past work is unsupervised namely TF and TF-IDF. There are several variants of TF and TFIDF and the most optimal variant has yet to be identified in the context of question classification based on BT. Therefore, this paper aims to study the TF, TF-IDF and normalized TF-IDF variants and identify the optimal variant that can enhance the exam question classification accuracy. To investigate the variants two different classifiers were used, which are Support Vector Machine (SVM) and Naïve Bayes. The average accuracies achieved by TF-IDF and normalized TF-IDF variants using SVM classifier were 64.3% and 72.4% respectively, while using Naïve Bayes classifier the average accuracies for TF-IDF and normalized TF-IDF were 61.9% and 63.0% respectively. Generally, the normalized TF-IDF variants outperformed TF and TF-IDF variants in accuracy and F1-measure respectively. Further statistical analysis using t-test and Wilcoxon Signed also shows that the differences in accuracy between normalized TF-IDF and TF, TF-IDF are significant. The findings from this study show that the Normalized TF-IDF3 variant recorded the highest accuracy of 74.0% among normalized TF-IDF variants. Also, the differences in accuracy between Normalized TF-IDF3 and other normalized variants are generally significant, thus the optimal variant is Normalized TF-IDF3. Therefore, the normalized TF-IDF3 variant is important for benchmarking purposes, which can be used to compare with other term weighting techniques in future work.

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“…where f (t, q) is number of term t appearing in a question q, this variant used in the past research [40].…”

Section: Variants Of Term Weightingmentioning

confidence: 99%

Identifying Optimal Baseline Variant of Unsupervised Term Weighting in Question Classification Based on Bloom Taxonomy

Sangodiah

San

Fui

et al. 2022

mendel

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“…The TF-IDF value expresses the relative importance of a specific term in the article and the entire corpus (Tongman & Wattanakitrungroj, 2018). This value is determined by two terms: the first one is the normalized term frequency and the second one is the inverse document frequency which is the logarithm of the number of the articles in the corpus divided by the number of articles where a specific term appears.…”

Section: Term Frequency-inverse Document Frequency Vectorsmentioning

confidence: 99%

A novel methodology for Arabic news classification

Alfonse

Gawich

2021

WIREs Data Min & Knowl

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The automated news classification concerns the assignment of news to one or more predefined categories. The automated classified news helps the search engines to mine and categorize the type of news that the user asks for. Most of the researchers focused on the classification of English news and ignore the Arabic news due to the complexity of the Arabic morphology. This article presents a novel methodology to classify the Arabic news. It relies on the use of features extraction and the application of machine learning classifiers which are the Naive Bayes (NB), the Logistic Regression (LR), the Random Forest (RF), the Xtreme Gradient Boosting (XGB), the K-Nearest Neighbors (KNN), the Stochastic Gradient Descent (SGD), the Decision Tree (DT), and the Multi-Layer Perceptron (MLP). The methodology is applied to the Arabic news dataset provided by Mendeley. The accuracy of the classification is more than 95%.

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Risk management disclosures and banks financial performance: evidence from emerging markets

Iqbal,

Sohail,

Irshad

et al. 2024

Risk Manag

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Classifying Positive or Negative Text Using Features Based on Opinion Words and Term Frequency - Inverse Document Frequency

Cited by 6 publications

References 9 publications

Identifying Optimal Baseline Variant of Unsupervised Term Weighting in Question Classification Based on Bloom Taxonomy

Identifying Optimal Baseline Variant of Unsupervised Term Weighting in Question Classification Based on Bloom Taxonomy

A novel methodology for Arabic news classification

Risk management disclosures and banks financial performance: evidence from emerging markets

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