ISSN 2587-814X (print), Russian version: ISSN 1998-0663 (print), |
Yuri Yekhlakov 1, Egor Gribkov2User opinion extraction model concerning consumer properties of products based on a recurrent neural network
2018.
No. 4 (46).
P. 7–16
[issue contents]
This article offers a long short-term memory (LSTM) based structured prediction model taking into account existing approaches to sequence tagging tasks and allowing for extraction of user opinions from reviews. We propose a model configuration and state transition rules which allow us to use past predictions of the model alongside sentence features. We create a body of annotated user reviews about mobile phones from Amazon for model training and evaluation. The model trained on reviews corpus with recommended hyperparameter values. Experiment shows that the proposed model has a 4.51% increase in the F1 score for aspects detection and a 5.44% increase for aspect descriptions compared to the conditional random field (CRF) model with the use of LSTM when F1 spans are matched strictly.
Citation:
Yekhlakov Yu.P., Gribkov E.I. (2018) User opinion extraction model concerning consumer properties of products based on a recurrent neural network.Business Informatics, no. 4 (46), pp. 7–16. DOI: 10.17323/1998-0663.2018.4.7.16 |
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