People readily express their opinions about the various products, companies, TV shows etc., on Twitter. These tweet messages are thus a rich source of information that can be exploited to understand the sentiments about the concerned products or services. Retrieving the tweets related to given entities is however a challenging task as their names are often (deliberately) ambiguous, e.g. Apple, Blackberry, Friends, etc. Nevertheless, identifying the relevant entities is an essential first step to develop reliable sentiment analysis techniques that is not considered in existing systems, for example: TweetFeel(http://www.tweetfeel.com), TwitterSentiment(http://twittersentiment.appspot.com) While there is a number of techniques for identifying named entities in unstructured text, they are often not directly applicable in this case, as tweet messages are very short (maximal 140 characters). This demonstrator introduces TweetSpector, a tool that addresses this retrieval task and enables to link tweet messages to a given entity. Our retrieval methods rely on classification techniques that exploit our concise descriptions of entity-relevant information, also called entity profiles.