User Sentiment Analysis of Solar Energy Monitoring Apps: Insights From Google Play Reviews

With a focus on user reviews from the Google Play Store, this study investigates user perceptions of mobile applications designed for remote monitoring and control of solar energy systems. This study uses sentiment analysis, a powerful natural language processing tool, to extract valuable information from structured and unstructured text data and classify opinions and comments as neutral, negative, or positive to understand user experiences, identify significant areas of satisfaction and dissatisfaction, and provide application developers with relevant information by analyzing more than 17,100 user comments across 18 applications that were updated in 2024, using the Google-Play-scraper Python script. The reviews have been analyzed using the Valence Aware Dictionary and Sentiment Reasoner (VADER), to determine whether they are positive, neutral, or negative. The analysis revealed that a large majority of users (67.4%) expressed primarily positive sentiments about the ease of use and effectiveness of these apps in monitoring solar systems; there are also about (15.1%) negative reviews, and (17.5%) neutral reviews. The analysis leverages classical supervised Machine Learning (ML) models in combination with lexicon-based sentiment analysis (VADER); to classify the sentiment expressed in comments. Support Vector Machine (SVM) is one of the best supervised machine learning methods that can be used for sentiment classification tasks, which achieved 91% accuracy, indicating the effectiveness of SVM in sentiment classification when compared to other machine learning methods, such as: Decision Tree (DT), Logistic Regression (LR), RidgeClassifier, Random Forest (RF), and Naive Bayes (NB) which achieved 90%, 89%, 87%, 68%, and 40% accuracy, respectively. However, the study also identified areas for improvement, such as addressing technical bugs, improving response times, and enhancing the user interface. These findings provide crucial insights for developers to improve user experience, enhance app functionality, and ultimately improve user satisfaction with solar monitoring apps. The findings also show that sentiment analysis is a useful technique for classifying and assessing reviews and comments from other people's reviews and feedback.