摘要
Microblogging provides a new platform for com- municating 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 mi- croblogging 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 ex- periments, we extract keywords for microblog users from the largest microblogging website in China, Sina Weibo. The re- suits show that our method can identify users＇ interests accu- rately and efficiently.
Microblogging provides a new platform for com- municating 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 mi- croblogging 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 ex- periments, we extract keywords for microblog users from the largest microblogging website in China, Sina Weibo. The re- suits show that our method can identify users＇ interests accu- rately and efficiently.
