Stance Analysis of Policies Related to Emission Test Obligations using Twitter Social Media Data

Abstract

Social media is currently widely used to disseminate various kinds of information, whether expressing feelings, or opinions. Public opinion is no exception regarding government policies and the implementation of emission tests, which describe the conditions that exist in society. Information on public opinion data obtained through social media in real time can assist the government in evaluating policies and improving the quality of currently implemented policies, particularly evaluating the implementation of emission tests on motorized vehicles. In this research, the application of stance analysis is used to evaluate emission test policies based on public opinion.In addition, this research aims to combine several machine learning methods and feature extraction methods to find the best combination based on accuracy, training time, and prediction time based on emission test policies. The best model based on the level of accuracy is a combination of Decision Tree and BERT, which reaches a value of 66%. Meanwhile, based on training time, the model that has the advantage is the Ridge Classifier with fasttext text representation. Based on prediction time, there are 3 combination models, namely Decision Tree with word2vec, SVM with Word2Vec, and Logistic Regression with fasttext text representation.

Boosting Institutional Identity on X Using NLP and Sentiment Analysis: King Faisal University as a Case Study

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