Abstract
In today's competitive mobile app landscape, understanding user feedback is crucial for enhancing the overall user experience (UX). However, manually analyzing user reviews can be a time-consuming and labor-intensive process. This article explores the application of state-of-the-art aspect-based sentiment analysis (ABSA) algorithms to automate user review analysis and provide actionable insights for developers.
Automating User Feedback Analysis
The effectiveness of mobile apps depends heavily on their ability to meet users' needs and expectations. To achieve this, app developers must have a deep understanding of user feedback, which can be scattered across various online platforms. Manually analyzing these reviews can be a daunting task, requiring significant time and resources. Fortunately, recent advances in natural language processing (NLP) and machine learning (ML) have made it possible to automate this process using ABSA algorithms.
Leveraging Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis is a powerful technique that enables developers to extract specific aspects or features of an app related to user feedback. This approach takes into account the complex relationships between various aspects, such as UI design, functionality, and overall performance. By analyzing these relationships, ABSA models can provide valuable insights into what users like or dislike about an app, helping developers make data-driven decisions for UX improvement.
Our Approach
To demonstrate the effectiveness of ABSA in automating user feedback analysis, we scraped relevant Google Play Store user reviews for six distinct applications across various categories. We then employed and fine-tuned a BERT-based ABSA model to extract sentiment triplets (aspect, opinion, polarity) from these review sentences. The results show that our framework can effectively capture user feedback by identifying specific aspects and sentiments related to app features and functionalities.
Unlocking Actionable Insights
Our framework, which utilizes the ABSA model along with filtering methods via Topic Modeling, can automatically extract sentiment triplets and provide additional suggestions and statistics for app developers. This enables them to make informed decisions for UX improvement, streamlining their development process and ultimately enhancing the overall user experience.
By leveraging the power of aspect-based sentiment analysis, mobile app developers can unlock valuable insights from user feedback, drive informed decision-making, and create more effective, user-centric applications.