It has become a prevalent lifestyle nowadays that netizens voice their opinions on social networks (Web 2.0), for matters of all sizes, and on a regular basis. The opinions which initially should be intended for their groups of friends propagate to all public users. This pond of opinions in the forms of forum posts, messages written on micro-blogs, Twitter and Facebook, are largely contributed by communities of online users (or sometimes bloggers). The messages though might seem to be trivial when each of them is viewed singularly, the converged sum of them serves as a potentially useful source of information to be analysed. A government of a city, for instance, may be interested to know the response of the citizens after a new policy is announced, from their voices collected from the Internet. However, such online messages are unstructured in nature, their contexts vary greatly, and that poses a tremendous difficulty in correctly interpreting them. In this paper we propose an innovative analytical model that evaluates such messages by representing them in different moods. The model comprises of several data analytics such as cultural moods analyzer implemented by neural networks, text mining and hierarchical visualization that reflects public moods over a large population of Internet comments.