Sentiment Analysis of Jobstreet Application Reviews on Google Play Store Using Support Vector Machine Algorithm with Adaptive Synthetic

Abstract

Abstract.

Purpose: This research aims to test the performance result of the Support Vector Machine (SVM) classification algorithm using the help of Adaptive Synthetic (ADASYN) oversampling to analyze sentiment in Jobstreet application reviews on the Google Play Store. Sentiment analysis is a significant method to understand the market needs and application improvement.

Methods/Study design/approach: The dataset originates from Google Play reviews gained using the scrapping method, comprising 5,174 reviews with 11 attributes. The process begins with data scrapping, data labeling, and data preprocessing, including casefolding, tokenizing, filtering, and stemming using Python programs. The data is then weighted and split using an 80:20 ratio. Then applying oversampling ADASYN on a clean dataset before using SVM classification to produce the performance result.

Result/Findings: Both scenarios are conducted on SVM classification to classify the dataset. The evaluation results indicate that using SVM classification without ADASYN produces an accuracy result of 89.08%. Other scenarios by using SVM classification with the ADASYN sampling approach produce an accuracy result of 89.95%. The performance in accuracy result by using the ADASYN sampling approach on SVM classification shows an increasing result of 0.87%.

Novelty/Originality/Value: This study employs two result scenarios of SVM classification by using the ADASYN sampling approach. It contributes to the literature by demonstrating the usability of the ADASYN oversampling approach to optimalize the SVM classification result used for sentiment analysis in Jobstreet application reviews on the Google Play Store.