Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Detecting experiences from weblogs
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Automatic organization of human task goals for web-scale problem solving knowledge
Proceedings of the seventh international conference on Knowledge capture
Automatic extraction of advice-revealing sentences foradvice mining from online forums
Proceedings of the seventh international conference on Knowledge capture
Toward advice mining: conditional random fields for extracting advice-revealing text units
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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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.