Harnessing Multi-label Classification Approaches for Economic Phenomena Categorization

Abstract

One fashion to report a country’s economic state is by compiling economic phenomena from several sources. The collected data may be explored based on their sentiments and economic categories. This research attempted to perform and analyze multiple approaches to multi-label text classification in addition to providing sentiment analysis on the economic phenomena. The sentiment and single-label category classification was performed utilizing the logistic regression model. Meanwhile, the multi-label category classification was fulfilled using a combination of logistic regression, support vector machines, k-nearest neighbor, naïve Bayes, and decision trees as base classifiers, with binary relevance, classifier chain, and label power set as the implementation approaches. The results showed that logistic regression works well in sentiment and single-label classification, with a classification accuracy of 80.08% and 92.71%, respectively. However, it was also discovered that it works poorly as a base classifier in multi-label classification, indicated by the classification accuracy dropping to 13.35%, 15.40%, and 30.65% for binary relevance, classifier chain, and label power set, respectively. Alternatively, naïve Bayes works best as a base classifier in the label power set approach for multi-label classification, with a classification accuracy of 63.22%, followed by decision trees and support vector machines.

Keywords

• classification
• economics
• machine learning
• performance analysis