Mining advices from weblogs

Alfan Farizki Wicaksono, Sung Hyon Myaeng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationCIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Pages2347-2350
Number of pages4
DOIs
Publication statusPublished - 19 Dec 2012
Event21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: 29 Oct 20122 Nov 2012

Publication series

NameACM International Conference Proceeding Series

Conference

Conference21st ACM International Conference on Information and Knowledge Management, CIKM 2012
Country/TerritoryUnited States
CityMaui, HI
Period29/10/122/11/12

Keywords

  • advice mining
  • text mining

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