One major piece of information available on the web is reviews about various products that are written by users. Some commercial websites provide additional information about the products along with the reviews. However, in the opinion mining research field, most existing methods have ignored this additional valuable information, thus influencing the accuracy of the mining results and the interpretation of various aspects related to the products. In this thesis, we consider the reviews obtained from Epinions.com related to cameras, and we propose ReviewMiner, an unsupervised method of automatically identifying useful aspects of a product and estimating the corresponding ratings for each aspect from the review texts. The method explores various linguistic patterns to extract potential aspects and context-dependent opinion phrases, and the technique employs a series of heuristic strategies and pruning techniques. Experimental results have demonstrated the effectiveness of the proposed techniques and shown their advantages over comparative baselines.