A Study of Sentiment Analysis on Mobile Applications

In the contemporary digital landscape, user feedback on platforms like the Google Play Store holds immense value for developers and consumers alike.However, manually analyzing the sentiment of a large volume of reviews can be time-consuming and tedious.To address this challenge, we present an automated approach leveraging web scraping with RSelenium and sentiment analysis with the AFINN lexicon to analyze reviews of a specified app on the Google Play Store.Our methodology involves using RSelenium, a web scraping tool, to navigate to the target app's page, extract reviews, and then perform sentiment analysis on the collected text data.We then employ the AFINN lexicon, which assigns sentiment scores to individual words, to quantify the sentiment expressed in each review.Positive and negative sentiments are categorized based on the polarity of the sentiment scores.The implementation involves several steps, including establishing a connection to a web browser using RSelenium, locating and extracting reviews from the Google Play Store page, cleaning and tokenizing the text data, and finally, conducting sentiment analysis and visualizing the results.The project's outcomes include insights into the overall sentiment of the reviews, highlighting aspects that users appreciate or find lacking in the app.These insights can inform developers' decisions regarding app improvements and feature enhancements.Additionally, the automation of sentiment analysis reduces the manual effort required, enabling efficient monitoring of user feedback, and facilitating data-driven decisionmaking processes.