Text analysis for email multi label classification
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Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
Harsha Kadam, Sanjit
Paniskaki, Kyriaki
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This master’s thesis studies a multi label text classification task on a small data
set of bilingual, English and Swedish, short texts (emails). Specifically, the size of
the data set is 5800 emails and those emails are distributed among 107 classes with
the special case that the majority of the emails includes the two languages at the
same time. For handling this task different models have been employed: Support
Vector Machines (SVM), Gated Recurrent Units (GRU), Convolution Neural Network
(CNN), Quasi Recurrent Neural Network (QRNN) and Transformers. The
experiments demonstrate that in terms of weighted averaged F1 score, the SVM
outperforms the other models with a score of 0.96 followed by the CNN with 0.89
and the QRNN with 0.80.
Beskrivning
Ämne/nyckelord
natural language processing , machine learning , multi label text classification , deep neural networks , bilingual texts , emails , short texts