Gender-Preferential Text Mining of E-mail Discourse
ACSAC '02 Proceedings of the 18th Annual Computer Security Applications Conference
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A comparative study of statistical features of language in blogs-vs-splogs
Proceedings of the second workshop on Analytics for noisy unstructured text data
Learning age and gender using co-occurrence of non-dictionary words from stylistic variations
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Hi-index | 0.00 |
We report results of stylistic differences in blogging for gender and age group variation. The results are based on two mutually independent features. The first feature is the use of slang words which is a new concept proposed by us for Stylistic study of bloggers. For the second feature, we have analyzed the variation in average length of sentences across various age groups and gender. These features are augmented with previous study results reported in literature for stylistic analysis. The combined feature list enhances the accuracy by a remarkable extent in predicting age and gender. These machine learning experiments were done on two separate demographically tagged blog corpus. Gender determination is more accurate than age group detection over the data spread across all ages but the accuracy of age prediction increases if we sample data with remarkable age difference.