In the digitized world today internet is tone of the main sources of the information. There are several e-commerce websites where people/customers discuss different aspects/issues of the product. All such website provides a platform for the consumers to discuss and provide their opinion about the product, its features and their services. These opinions and reviews of the consumers provide very rich information both for other users as well as firms. But the issue with this information is that the information is mostly unorganized and therefore it is difficult to create a knowledge base out of it. What this paper propose is a product facet ranking framework which automatically determines the important facets of the product from the online comments/reviews, aiming to improve the usability of the consumer reviews. The facets are identified by the following observations; 1) The important facets of a product are usually given by several consumers in the review. 2) The review/opinion of the consumer on important facet of the product influences the overall opinion or view of the consumer on the product. But identifying the most important facets will increase the usefulness of the innumerable reviews/opinions and is useful to both users and the firms itself. It is practically difficult for the people to manually identify the important facets of the products from the consumer reviews/opinions. Consumer can easily make purchasing decision by focusing more on the important features, while the firms can concentrate on improving the quality of the features or facets of the product.

1. W. Jin and H. H. Ho, "A novel lexicalized HMMbased learning framework for web opinion mining," in Proc. 26th Annu. ICML,Montreal, QC, Canada, 2009, pp. 465–472
2. Bing Liu, "Sentiment Analysis and Opinion Mining" pp. 7-140,2012.
3. M. Hu and B. Liu, "Mining and summarizing customer reviews," in Proc. SIGKDD, Seattle, WA, USA, 2004, pp. 168–177.
4. A. Finn, Kushmerick, N. , and Smyth, B. 2002. Genre Classification and Domain Transfer for Information Filtering. In Proc. of European Colloquium on Information Retrieval Research, pages 353-362.
5. V. Gupta and G. S. Lehal, "A survey of text summarization extractive techniques," J. Emerg. Technol. Web Intell. , vol. 2, no. 3, pp. 258–268, 2010.
6. T. L. Wong and W. Lam, "Hot item mining and summarization from multiple auction web sites," in Proc. 5th IEEE ICDM, Washington, DC, USA, 2005, pp. 797–800.
7. Chetan Mate, "Product Aspect Ranking using Sentiment Analysis: A Survey"in IRJET vol. 03, Issue 01, pp 126-127.
8. B. Liu, M. Hu, and J. Cheng, "Opinion observer: Analyzing and comparing opinions on the web," in Proc. 14th Int. Conf. WWW, Chiba, Japan, 2005, pp. 342–351.
9. Finn Årup Nielsen, March 2011 http://www2. imm. dtu. dk/pubdb/views/publication_details. php?id=6010
10. Bing Liu, "Sentiment Analysis and Subjectivity" Department of Computer Science, University of Illinois at Chicago, Handbook of Natural Language Processing, Second Edition
11. Md. Ansarul Haque, Tamjid Rahman "Sentiment Analysis by Using Fuzzy Logic" Department of Computer Science and Engineering, Stamford University, Bangladesh. IJCSEIT, Vol 4_No, 1, February, 2014

Keywords Are Your Own Designated Keywords Which Can Be Used For Easy Location Of The Manuscript Using Any Search Engines.