In the domain of application stores and marketplaces, user reviews are crucial for supporting multiple requirements engineering tasks. Feature extraction, emotion classification, topic analysis, review type identification, and polarity analysis are key components in requirements prioritization, feedback gathering, and release planning. Empirical evaluation of these techniques is challenging due to data collection complexities and a lack of reproducible methods and available tools. Furthermore, existing studies often focus on isolated tasks, hindering a comprehensive analysis of user perceptions. This paper introduces RE-Miner 2.0, a work-in-progress tool that integrates multiple data extraction and analysis methods in a distributed environment (RE-Miner Ecosystem), enabling a multidimensional and detailed analysis of user feedback. It offers a web-based service for task integration and comparison, supported by persistent storage and a web application that allows analytical visualization of reviews. As a result, RE-Miner 2.0 provides a platform for task integration, replication, and comparison of review mining techniques. Bringing advancements in deep review analysis for requirements engineering. A demo of the tool is showcased here: https://www.youtube.com/watch?v=a11bHSCYqqM.