Text Classification to Detect Student Level of Understanding in Prior Knowledge Activation Process

Wenando, Febby Apri; Adji, Teguh Bharata; Ardiyanto, Igi

doi:10.1166/asl.2017.8768

Cited by 15 publications

(9 citation statements)

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“…Therefore, it is recommended to apply several different classifiers on a master feature vector to observe which one reaches to the better results. Hence, we selected eight different classifiers NB [12], SVM [14], KNN [15], DT [16], RF [13], AdaBoost [17], MLP [18] and LR [19].…”

Section: E Machine Learning Modelsmentioning

confidence: 99%

Automatic Hate Speech Detection using Machine Learning: A Comparative Study

Abro¹,

Shaikh²,

Hussain³

et al. 2020

IJACSA

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The increasing use of social media and information sharing has given major benefits to humanity. However, this has also given rise to a variety of challenges including the spreading and sharing of hate speech messages. Thus, to solve this emerging issue in social media sites, recent studies employed a variety of feature engineering techniques and machine learning algorithms to automatically detect the hate speech messages on different datasets. However, to the best of our knowledge, there is no study to compare the variety of feature engineering techniques and machine learning algorithms to evaluate which feature engineering technique and machine learning algorithm outperform on a standard publicly available dataset. Hence, the aim of this paper is to compare the performance of three feature engineering techniques and eight machine learning algorithms to evaluate their performance on a publicly available dataset having three distinct classes. The experimental results showed that the bigram features when used with the support vector machine algorithm best performed with 79% off overall accuracy. Our study holds practical implication and can be used as a baseline study in the area of detecting automatic hate speech messages. Moreover, the output of different comparisons will be used as state-of-art techniques to compare future researches for existing automated text classification techniques.

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Section: E Machine Learning Modelsmentioning

confidence: 99%

Automatic Hate Speech Detection using Machine Learning: A Comparative Study

Abro¹,

Shaikh²,

Hussain³

et al. 2020

IJACSA

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“…Therefore, it is suggested to apply several classifiers on a master numerical vector to see which one achieves better results. Hence, we chose five different classifiers Naïve Bayes (NB) [24], Support Vector Machine (SVM) [25], Random Forest (RF) [26], Logistic Regression (LR) [27], and Ensemble in approach 1. Whereas in approach 2, we have chosen SVM and NB classifiers.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Aspect Based Sentimental Analysis of Hotel Reviews: A Comparative Study

Abro¹,

Shaikh

Abro³

et al. 2020

SJCMS

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The increasing use of the internet enables users to share their opinion about what they like and dislike regarding products and services. For efficient decision making, there is a need to analyze these reviews. Sentiment analysis or opinion mining is commonly used to detect polarity (positive or negative) of reviews. But, it does not show the aspect or orientation of the text. In this study, state-of-art approaches based on supervised machine learning employed to perform three tasks on the dataset provided by SemEval. Tasks A and B are related to predicting the aspect of the restaurant’s reviews, whereas task C shows their polarity. Additionally, this study aims to compare the performance of two feature engineering techniques and five machine learning algorithms to evaluate their performance on a publicly available dataset named SemEval-2015 Task 12. The experimental results showed that the word2vec features when used with the support vector machine algorithm outperformed by giving 76%, 72% and 79% off overall accuracies for Task A, Task B, and Task C respectively. Our comparative study holds practical significance and can be used as a baseline study in the domain of aspect-based sentiment analysis.

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“…Support Vector Machines (SVM) has the best performance (F-measure) as compared to Naïve Bayes, DT, and KNN to classify the terrorism events from the terrorism news article corpus in order to assist the Thai terrorism events extraction [7]. Logistic Regression (LR) has the best performance (accuracy, kappa and time of classification process) as compared to Bayes Net, Naïve Bayes, Simple Logistic, Decision Tree (J48 & J48 Graft), and Multi-Layer Perceptron to classify paragraph text citation for Prior Knowledge Activation [8]. All text classification task use the TF-IDF method for data text representation [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

“…Logistic Regression (LR) has the best performance (accuracy, kappa and time of classification process) as compared to Bayes Net, Naïve Bayes, Simple Logistic, Decision Tree (J48 & J48 Graft), and Multi-Layer Perceptron to classify paragraph text citation for Prior Knowledge Activation [8]. All text classification task use the TF-IDF method for data text representation [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, this research aims to find the best estimator of IHT to solve imbalanced data problem in text classification, especially in the case of hate speech classification. The best classifiers from previous researches (KNN, DT, SVM and LR) are chosen to be IHT estimators in this research [5]- [8]. The TF-IDF method is used in this research for data text representation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Study of Undersampling Method: Instance Hardness Threshold with Various Estimators for Hate Speech Classification

Verdikha

Adji

Permanasari

2018

IJITEE

Self Cite

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A text classification system is needed to address the problem of hate speech in social media. However, texts of hate speech are very hard to find in social media. This will make the distribution of training data to be unbalanced (imbalanced data). Classification with imbalanced data will make a poor performance. There are several methods to solve the problem of classification with imbalanced data. One of them is undersampling with Instance Hardness Threshold (IHT) method. IHT method balances the dataset by eliminating data that are frequently misclassified. To find those data, IHT requires an estimator, which is a classifier. This research aims to compare estimators of IHT method to solve imbalanced data problem in hate speech classification using TF-IDF weighting method. This research uses the class ratio of dataset after undersampling, time of the undersampling process, and Index of Balanced Accuracy (IBA) evaluation to determine the best IHT method. The results of this research show that IHT method using the Logistic Regression (IHT(LR)) has the fastest undersampling process (1.91 s), perfectly balance dataset with the class ratio is 1:1, and has the best of IBA evaluation in all estimation process. This result makes IHT(LR) be the best method to solve the imbalanced data problem in hate speech classification.

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Text Classification to Detect Student Level of Understanding in Prior Knowledge Activation Process

Cited by 15 publications

References 0 publications

Automatic Hate Speech Detection using Machine Learning: A Comparative Study

Automatic Hate Speech Detection using Machine Learning: A Comparative Study

Aspect Based Sentimental Analysis of Hotel Reviews: A Comparative Study

Study of Undersampling Method: Instance Hardness Threshold with Various Estimators for Hate Speech Classification

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