Stylometric Analysis of Parliamentary Speeches: Gender Dimension Justina Mandravickaite˙ Tomas Krilaviciusˇ Vilnius University, Lithuania Vytautas Magnus University, Lithuania Baltic Institute of Advanced Baltic Inistitute of Advanced Technology, Lithuania Technology, Lithuania
[email protected] [email protected] Abstract to capture the differences in the language due to the gender (Newman et al., 2008; Herring and Relation between gender and language has Martinson, 2004). Some results show that gen- been studied by many authors, however, der differences in language depend on the con- there is still some uncertainty left regard- text, e.g., people assume male language in a for- ing gender influence on language usage mal setting and female in an informal environ- in the professional environment. Often, ment (Pennebaker, 2011). We investigate gender the studied data sets are too small or texts impact to the language use in a professional set- of individual authors are too short in or- ting, i.e., transcripts of speeches of the Lithua- der to capture differences of language us- nian Parliament debates. We study language wrt age wrt gender successfully. This study style, i.e., male and female style of the language draws from a larger corpus of speeches usage by applying computational stylistics or sty- transcripts of the Lithuanian Parliament lometry. Stylometry is based on the two hypothe- (1990–2013) to explore language differ- ses: (1) human stylome hypothesis, i.e., each in- ences of political debates by gender via dividual has a unique style (Van Halteren et al., stylometric analysis. Experimental set 2005); (2) unique style of individual can be mea- up consists of stylistic features that indi- sured (Stamatatos, 2009), stylometry allows gain- cate lexical style and do not require exter- ing meta-knowledge (Daelemans, 2013), i.e., what nal linguistic tools, namely the most fre- can be learned from the text about the author quent words, in combination with unsu- - gender (Luyckx et al., 2006; Argamon et al., pervised machine learning algorithms.