The Web has dramatically changed the way we express opinions on certain products that we have purchased and used, or for services that we have received in the various industries. Opinions and reviews can be easily posted on the Web, such as in merchant sites, review portals, blogs, Internet forums, and much more. These data are commonly referred to as user-generated content or user-generated media. Both the product manufacturers, as well as potential customers are very interested in this online dasiaword-of-mouthpsila, as it provides product manufacturers information on their customers likes and dislikes, as well as the positive and negative comments on their products whenever available, giving them better knowledge of their products limitations and advantages over competitors; and also providing potential customers with useful and dasiafirst-handpsila information on the products and/or services to aid in their purchase decision making process. This paper discusses the existing works on opinion mining and sentiment classification of customer feedback and reviews online, and evaluates the different techniques used for the process. It focuses on the areas covered by the evaluated papers, points out the areas that are well covered by many researchers and areas that are neglected in opinion mining and sentiment classification which are open for future research opportunity.