Textual information in the world can be broadly categorized into two main types: facts and opinion. Facts are objective expression about entities and their properties. Opinions are usually subjective expression that describe people’s sentiments, appraisals or feelings toward entities, event and their properties. Numerous consumer reviews of products are now available on the Internet. Consumer reviews contain rich and valuable knowledge for both ﬁrms and users. However, the reviews are often disorganized, leading to difficulties in information navigation and knowledge acquisition. This article proposes a product aspect ranking framework, which automatically identiﬁes the important aspects of products from online consumer reviews, aiming at improving the usability of the numerous reviews. The important product aspects are identiﬁed based on two observations: (a) the important aspects are usually commented by a large number of consumers; and (b) consumer opinions on the important aspects greatly inﬂuence their overall opinions on the product. In particular, given the consumer reviews of a product, we ﬁrst identify product aspects by a shallow dependency parser and determine consumer opinions on these aspects via a sentiment classiﬁer. We then develop a probabilistic aspect ranking algorithm to infer the importance of aspects by simultaneously considering aspect frequency and the inﬂuence of consumer opinions given to each aspect over their overall opinions. Social media is playing a growing role in providing consumer feedback to companies about their product and services to maximize the benefits of this feedback, companies want to know how different consumer segments they are interested in, such as Products, Articles, and Comic book fans react to their products and campaigns We investigate models based on sentiment analysis based on Amazon reviews and their application on reviews from other sources using a bag-of-words model with weights calculated using logistic regression. We examine different methods for adjusting unbalanced datasets as well as the qualitative performance of different features such as unigram and bigrams when applied to reviews from different sources. We also present a method for adjusting entity weights when making quantitative presentations of the polarity of nouns.