Micro-blogging today has become a very popular communication tool among the Internet users. Real-time web services such as Twitter allow users to express their opinions and interests, often expressed in the form of short text messages. Many business companies are looking into utilizing these data streams in order to improve their marketing campaigns, refine advertising and better meet their customer needs. In this study, we focus on using Twitter, for the task of extraction product reputation trend. Thus, business could gauge the effectiveness of a recent marketing campaign by aggregating user opinions on Twitter regarding their product. In this paper, we introduce an approach for automatically classifying the sentiment of Twitter messages toward product/brand, using emoticons and by improving pre-processing steps in order to achieve high accuracy.