Sentiment Analysis of Electricity Company Service Quality Using Naïve Bayes

Abstract

In facing the era of technological disruption, a large company providing electricity in Indonesia, namely PT PLN is transforming to digitize all business processes and improve the quality of customer service. PLN Mobile application was developed in December 2020, and 18 million users have downloaded it. PLN Mobile application provides various electrical services for users. There are a lot of online opinions today. Organizations need to know the public perception of their product or service, sales projections, and customer happiness. Our research will identify public opinion (positive and negative) about PLN Mobile Application using sentiment analysis by taking review data from Google Play Store. Sentiment analysis is classified using Naïve Bayes and analyzed based on the dimensions of the quality of electricity services: empathy, responsiveness, and reliability. The results of this study indicate that Naïve Bayes is quite well used for binomial labels (positive and negative) with an accuracy of 73%. Still, for service quality dimensions, the accuracy is 45%. Indonesian language datasets are quite difficult to process due to non-standard language, foreign words, mixed language variations, and abbreviations. Determination of ground truth or manual labeling requires consistency and skilled personnel to determine the context of the text data to obtain a model with optimal performance. This study informs the classification of each dimension of the quality of electricity services in Indonesia based on positive and negative sentiment data for PLN Mobile Application users. Reliability received the most negative sentiments. This can be used for PT PLN to improve the quality-of-service reliability to customers.