Product discovery is crucial for any retailer. Traditional approaches to achieve this involve understanding queries. Current methods for query understanding require knowledge of product attributes. However, the attributes of interest may not be known beforehand. In addition, these attributes may evolve over time. This study aims to identify significant attributes of the product from descriptions, reviews, and other sources such as articles about the products. A phrase mining technique is used to extract relevant and important words and phrases. Meaningful concepts are generated by clustering these words and phrases. Knowledge of existing product attributes and their corresponding values is utilized as constraints during the clustering process. To uncover new attributes, we utilize topic modeling. This study presents a novel topic modeling framework that explicitly handles constraints. Experimental results from real-world examples and a benchmark dataset demonstrate the efficacy of the framework.