| Abstract: | Microblogging provides a new platform for communicating and sharing information among Web users. Users can express opinions
and record daily life using microblogs. Microblogs that are posted by users indicate their interests to some extent. We aim
to mine user interests via keyword extraction from microblogs. Traditional keyword extraction methods are usually designed
for formal documents such as news articles or scientific papers. Messages posted by microblogging users, however, are usually
noisy and full of new words, which is a challenge for keyword extraction. In this paper, we combine a translation-based method
with a frequency-based method for keyword extraction. In our experiments, we extract keywords for microblog users from the
largest microblogging website in China, Sina Weibo. The results show that our method can identify users’ interests accurately
and efficiently. |
