Enriching feature engineering for short text samples by language time series analysis

EPJ Data Science

Table 1 Overview of representative authorship attribution problems. References [15, 28–39] discuss vector space models [9]

Paper	Language	Text style	Average sample text length (words)	Number of samples	Number of classes
[15, 16, 40]	English	Federalist Papers	900 to 3500	85	3
[33]	English	Newspaper articles	89***	112	50
[33]	English	Newspaper articles	714****	14	50
[28]	English	Incriminating digital documents	290	69	10
[29]	Modern Greek	Digital messages	1209	250	10
[29]	Modern Greek	Newspaper articles	1007.5	400	20
[30]	Modern Greek	Greek Parliament	1590	341	5
		Register	2871	127	5
		Register	1285	1005	5
[31]	German	Newspaper articles	438	1200	2*
			480	550	2*
			357	3233	2*
[32]	English	Digital messages	169	300 to 400	10
[32]	Chinese	Digital messages	807**	300 to 400	10
[34]	English	Book chapters	N/A	1960 to 2450	15
[35]	English	Variate types from the ad-hoc authorship attribution contest	Hundreds to thousands	7 to 38	3 to 13
[36]	English	Works of Shakespeare and Fletcher	1000	100	2
[37]	Belgian	Newspaper articles	600	300	3
[38]	Modern Greek	Newspaper articles	866.8	200	10
[38]	Modern Greek	Newspaper articles	1148.2	200	10
[39]	English	Novels written by Bronte Sisters	1000	480	2
			500	942	2
			200	2232	2
[41]	English	Twitter, blog, review, novel, and essay	127 to 7078	192 to 400	2*****
[42]	English	Works by Shakespeare, Christopher Marlowe, and Elizabeth Cary	N/A	57	3
[43]	Persian	Books	N/A	36	5
[44]	English	Books	N/A	80	8
				80	8
				80	8
[45]	English	Books	N/A	100	20
[46]	English	Books	20,000	100	10