Web forums often contain explicit key learnings gleaned from people's experiences since they are platforms for personal communications on sharing information with others. One of the key learnings contained in Web forums is often expressed in the form of advice. As part of human experience mining from Web resources, we aim to provide a methodology to extract advice-revealing sentences from Web forums due to its usefulness, especially in travel domain. Instead of viewing the problem as a simple classification, we define it as a sequence labeling problem using various features. We identify three different types of features (i.e., syntactic features, context features, and sentence informativeness) and propose a new way of using Hidden Markov Model (HMM) for labeling sequential sentences, which in our experiment gave the best performance for our task. Moreover, the sentence informativeness score serves as an important feature for this task. It is worth noting that this work is the first attempt to extract advice-revealing sentences from Web forums.