Text Mining Approach Using TF-IDF and Naive Bayes for Classification of Exam Questions Based on Cognitive Level of Bloom's Taxonomy

Aninditya, Annisa; Hasibuan, Muhammad Azani; Sutoyo, Edi

doi:10.1109/iotais47347.2019.8980428

Cited by 36 publications

(30 citation statements)

References 14 publications

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“…Opinions play an important role as product feedback, services, and other topics. Various text mining and sentiment analysis using classification approaches [19][20][21][22] such as Naïve Bayes classifier [23][24][25], logistic regression [26,27], support vector machine [28,29], and K-nearest neighbor [30] has been applied for finding the best result and accuracy. Therefore, in this study, sentiment analysis will be conducted to find out public opinion about the discourse of relocation of the capital city from Jakarta to Penajam Paser Utara and Kutai Kartanegara.…”

Section: Introductionmentioning

confidence: 99%

Twitter sentiment analysis of the relocation of Indonesia's capital city

Sutoyo

Almaarif

2020

Bulletin EEI

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Indonesia has a capital city which is one of the many big cities in the world called Jakarta. Jakarta's role in the dynamics that occur in Indonesia is very central because it functions as a political and government center, and is a business and economic center that drives the economy. Recently the discourse of the government to relocate the capital city has invited various reactions from the community. Therefore, in this study, sentiment analysis of the relocation of the capital city was carried out. The analysis was performed by doing a classification to describe the public sentiment sourced from twitter data, the data is classified into 2 classes, namely positive and negative sentiments. The algorithms used in this study include Naïve Bayes classifier, logistic regression, support vector machine, and K-nearest neighbor. The results of the performance evaluation algorithm showed that support vector machine outperformed as compared to 3 algorithms with the results of Accuracy, Precision, Recall, and F-measure are 97.72%, 96.01%, 99.18%, and 97.57%, respectively. Sentiment analysis of the discourse of relocation of the capital city is expected to provide an overview to the government of public opinion from the point of view of data coming from social media.

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Section: Introductionmentioning

confidence: 99%

Twitter sentiment analysis of the relocation of Indonesia's capital city

Sutoyo

Almaarif

2020

Bulletin EEI

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“…As the first study to examine this text classification task, it started by developing a dataset of more than 600 questions. This is larger than the size of any of the other datasets that have been used in previous studies to classify English questions [17], [19], [41]. The dataset includes questions from different academic disciplines.…”

Section: Discussionmentioning

confidence: 99%

“…Other types of questions, such as multiple-choice, problem-based, and true/false, were not included. While this is the norm for similar datasets that have been developed for English questions [17], [19], [41], [48], the absence of such questions would limit the application of the proposed method.…”

Section: Discussionmentioning

confidence: 99%

“…They used WordNet and cosine similarity to develop a rulebased classifier, which was able to achieve 70% accuracy. Similarly, Aninditya, et al [19] developed a dataset of 300 mid-term and final exam questions taken from different information technology courses. They then used TF-IDF for feature extraction and naive Bayes for classification.…”

Section: Related Workmentioning

confidence: 99%

“…The selection of these five classifiers was for several reasons. First, the five classifiers were previously used for classifying assessment questions, and they all performed very well in the classification task [17], [19], [41]. These five approaches were also used for classifying Arabic text, and their performance was very good [50]- [52].…”

Section: Step 4: Classificationmentioning

confidence: 99%

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Arabic Questions Classification Using Modified TF-IDF

Alammary

2021

IEEE Access

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Classifying the cognitive levels of assessment questions according to Bloom's taxonomy can help instructors design effective assessments that are well aligned with the intended learning outcomes. However, the classification process is time consuming and requires experience. Many studies have attempted to automate the process by utilizing different machine learning and text mining approaches, but none has examined the classification of Arabic questions. The purpose of this study is to examine this research gap and to introduce a new feature extraction method that would better suit Arabic questions and their unique characteristics. It also aims to provide Arab instructors with a tool that can help them automatically classify their assessment questions. To accomplish this purpose, the study developed a dataset of more than 600 Arabic assessment questions. It then proposed a modified term frequency-inverse document frequency (TF-IDF) method for extracting features from Arabic questions. Unlike the traditional TF-IDF, the proposed method was designed to take the nature of assessment questions into consideration. It was evaluated by comparing it to two methods that have been used for classifying English questions, i.e., the traditional TF-IDF and a modified TF-IDF method called term frequency part-of-speech-inverse document frequency (TFPOS-IDF). A t-test was utilized to examine whether the difference in performance between the three methods was statistically significant. The proposed method outperformed the two other methods. The overall accuracy, precision, and recall scored by the proposed method were significantly higher than those scored by the traditional TF-IDF and TFPOS-IDF methods. The evaluation results indicate the promising potential of the proposed method, which can be extended to other languages.

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