Stance Classification with Improved Elementary Classifiers Using Lemmatization (Grand Challenge)

Priyanshu, Aman; Das, Vedant Rishi; Moghe, Shashank Rajiv; Rathod, Harsh; Medicherla, Sai Sravan; Chhabra, Mini Shail; Shastri, Sarthak

doi:10.1109/bigmm50055.2020.00077

Cited by 2 publications

(1 citation statement)

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“…The study used SVM for classification and Krippendorff's alpha value (KAV) to verify the classification performance. Priyanshu et al [22] used several machine learning methods and compared them for stance classification on the #MeTooMA dataset. The study realized that data imbalance occurred but did not apply any method to overcome it.…”

Section: Literature Reviewmentioning

confidence: 99%

Overcoming Data Imbalance Problems in Sexual Harassment Classification with SMOTE

Putrada

Wijaya²,

Oktaria³

2022

ijoict

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Delivery of justice with the help of artificial intelligence is a current research interest. Machine learning with natural language processing (NLP) can classify the types of sexual harassment experiences into quid pro quo (QPQ) and hostile work environments (HWE). However, imbalanced data are often present in classes of sexual harassment classification on specific datasets. Data imbalance can cause a decrease in the classifier's performance because it usually tends to choose the majority class. This study proposes the implementation and performance evaluation of the synthetic minority over-sampling technique (SMOTE) to improve the QPQ and HWE harassment classifications in the sexual harassment experience dataset. The term frequency-inverse document frequency (TF-IDF) method applies document weighting in the classification process. Then, we compare naïve Bayes with K-Nearest Neighbor (KNN) in classifying sexual harassment experiences. The comparison shows that the performance of the naïve Bayes classifier is superior to the KNN classifier in classifying QPQ and HWE, with AUC values of 0.95 versus 0.92, respectively. The evaluation results show that by applying the SMOTE method to the naïve Bayes classifier, the precision of the minority class can increase from 74% to 90%.

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Section: Literature Reviewmentioning

confidence: 99%