Name
Abstract | ||
|---|---|---|
The aim of this paper is to investigate the feasibility of predicting the gender of a text document's author using linguistic evidence. For this purpose, term- and style-based classification techniques are evaluated over a large collection of chat messages. Prediction accuracies up to 84.2% are achieved, illustrating the applicability of these techniques to gender prediction. Moreover, the reverse problem is exploited, and the effect of gender on the writing style is discussed. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11890393_29 | ADVIS |
Keywords | Field | DocType |
reverse problem,linguistic evidence,text document,gender prediction,style-based classification technique,writing style,chat mining,chat message,large collection | Information system,Feature selection,Computer science,Writing style,Artificial intelligence,Natural language processing,Text document,Distributed computing | Conference |
Volume | ISSN | ISBN |
4243 | 0302-9743 | 3-540-46291-0 |
Citations | PageRank | References |
11 | 0.77 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tayfun Kucukyilmaz | 1 | 56 | 4.10 |
| B. Barla Cambazoglu | 2 | 735 | 38.87 |
| Cevdet Aykanat | 3 | 996 | 84.08 |
| Fazli Can | 4 | 581 | 94.63 |