Document Type

Dissertation

Rights

This item is available under a Creative Commons License for non-commercial use only

Disciplines

Computer Sciences

Publication Details

Dissertation submitted in partial fulfilment of the requirements of Dublin Institute of Technology for the degree of M.Sc. in Computing (Stream), January 2018.

Abstract

The thesis aims to take the first step towards automated extraction of the information found in book reviews, by using machine learning tools to assign a label of fiction or non fiction to the text. The thesis makes use of neural networks and performs experiments around architecture, hyper-parameters and text processing from which an optimized model is produced. The thesis enjoys certain successes; it was possible to match the state of the art achieved by (Kim, 2014) and computation was sped up considerably from the default to the optimized model by 13.8 seconds per 50 steps. Further it is confirmed by the thesis that labelling a sequence as fiction or non fic tion can be performed most accurately with LSTM architectures and that contrary to (Reimers & Gurevych, 2017) every considered hyper parameter had a considerable impact on results.

DOI

10.21427/D77B97

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