Analytics-as-a-Service (AaaS) has become indispensable because it affords stakeholders to discover knowledge in Big Data. Previously, data stored in data warehouses follow some schema and standardization which leads to efficient data mining. However, the "Big Data" epoch has witnessed the rise of structured, semi-structured, and unstructured data, a trend that motivated enterprises to employ the NoSQL data storages to accommodate the high-dimensional data. In this paper, we introduce an AaaS tool that aims at accomplishing terms and topics extraction and organization from unstructured data sources such as NoSQL databases and textual contents (e.g., websites). The primary accomplishment in this paper is the detail justification of the architectural design of our proposed framework. This includes the proposed algorithms (e.g., concurrency search, linear search, etc.) and the performance of macro tasks such as filtering, tagging, and so on.