Abstract
The aim of this research is to find out how to perform effective clustering of unlabeled personal blog posts written in English by gender. Given a gender-labeled blog corpus and a blog corpus that is not gender-labeled, we extracted from the labeled corpus distinguishable unigrams for both males and females. Then, we defined two general features that represent the relative frequencies of the distinguishable malesâ unigrams and femalesâ unigrams, (malesâ frequency and femalesâ frequency). The best distinguishable feature was found to be the malesâ frequency feature with a ratio factor at least 1.4 times that of females. This feature leads to accuracy rate of 83.7% for gender clustering of the unlabeled blog corpus. To the best of our knowledge, this study presents two novelties: (1) this is the first study to cluster blog posts by gender, and (2) clustering of an unlabeled corpus using distinguishable features that were extracted from a labeled corpus.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.