RT Monograph
SR 00
A1 Gadat, Sébastien
A1 Villeneuve, Stéphane
T1 Parsimonious Wasserstein Text-mining
YR 2023
FD 2023-09
VO 23-1471
SP 20
K1 Natural Language Processing
K1 Textual Analysis
K1 Wasserstein distance
K1 clustering
AB This document introduces a parsimonious novel method of processing textual data based on the NMF factorization and on supervised clustering withWasserstein barycenter’s to reduce the dimension of the model. This dual treatment of textual data allows for a representation of a text as a probability distribution on the space of profiles which accounts for both uncertainty and semantic interpretability with the Wasserstein distance. The full textual information of a given period is represented as a random probability measure. This opens the door to a statistical inference method that seeks to predict a financial data using the information generated by the texts of a given period.
T2 TSE Working Paper
PB TSE Working Paper
PP Toulouse
AV Published
LK https://publications.ut-capitole.fr/id/eprint/48255/
UL http://tse-fr.eu/pub/128497