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Gender Profiling for Slovene Twitter communication: the Influence of Gender Marking, Content and Style

Ben Verhoeven, Iza Škrjanec, Senja Pollak
2017 Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing  
Inspired by the TwiSty corpus and experiments , we employed the Janes corpus and its gender annotations to perform gender classification experiments on Twitter text comparing a token-based and a lemma-based  ...  We present results of the first gender classification experiments on Slovene text to our knowledge.  ...  Acknowledgements The work described in this paper was partially funded by the Slovenian Research Agency within the national basic research project Resources, Tools and Methods for the Research of Nonstandard  ... 
doi:10.18653/v1/w17-1418 dblp:conf/acl-bsnlp/VerhoevenSP17 fatcat:72c4tt26wbg5remxhc6x5mjz4y

Stress Level Assessment of an Individual using Neural Networks based on Tweets

Astha Sheetal Upadhye, Prof. Yamuna U, Dayasagara V G, Kavana N Bhatt, Megha Pai M
2018 Zenodo  
In this paper, we propose a system similar to twitter that analyzes the stress level of an individual which can be viewed by the admin.  ...  Stress in workplace is deteriorating both physical and mental health of an individual. It also makes the individual less productive and less efficient in work.  ...  Twisty: A multilingual twitter stylometry corpus for gender and personality profiling.  ... 
doi:10.5281/zenodo.1732617 fatcat:y7aa6ttp2zb57n7yfmgnrvzjwy

What demographic attributes do our digital footprints reveal? A systematic review

Joanne Hinds, Adam N. Joinson, David Garcia
2018 PLoS ONE  
For each of the demographic attributes identified, we provide a database containing the platforms and digital traces examined, sample sizes, accuracy measures and the classification methods applied.  ...  Finally, we discuss the main research trends/findings, methodological approaches and recommend directions for future research.  ...  Twisty: a Multilingual Twitter Stylometry Corpus for Gender and Personality Profiling. Proc 10th Lang Resour Eval Conf (LREC 2016). 2016; 81. Verhoeven, B., Š krjanec, I., & Pollak S.  ... 
doi:10.1371/journal.pone.0207112 pmid:30485305 pmcid:PMC6261568 fatcat:jo2ycsiit5a23drbbpytifn36q