Mining Advices from Weblogs

Abstract

Weblog, one of the fastest growing user generated contents, often contains key learnings gleaned from people's past experiences which are really worthy to be well presented to other people. One of the key learnings contained in weblogs is often vented in the form of advice. In this paper, we aim to provide a methodology to extract sentences that reveal advices on weblogs. We observed our data to discover the characteristics of advices contained in weblogs. Based on this observation, we define our task as a classification problem using various linguistic features. We show that our proposed method significantly outperforms the baseline. The presence or absence of imperative mood expression appears to be the most important feature in this task. It is also worth noting that the work presented in this paper is the first attempt on mining advices from English data.