Sentiment analysis on review data of MyHRMIS mobile using lexicon-based approach

This paper reports the user sentiment towards MyHRMIS Mobile application using the lexicon-based approach. The total number of 2184 reviews were scraped from the Google Play Store and App Store. Following the pre-processing procedures, a cleaned dataset consisting of 2144 reviews was retained. The lexicon-based approach used in the study is the VADER method and the SentiWordNet-based approach to label a review dataset, either positive or negative. Then SVM is applied to the labeled dataset for testing and comparing the performance of the methods. The result of the performance evaluation shows that using the VADER method outperforms SentiWordNet-based approach with an accuracy value of 91.39, a precision value of 91.61, and a recall value of 98.65. The VADER method demonstrated efficient and quick classification of substantial volumes of data. Hence, we suggested that the VADER method can be used to extract sentiment from MyHRMIS Mobile application review data instead of SentiWordNet-based approach.

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