Extraction of aspects from Online Reviews Using a Convolution Neural Network

Autor:	Kamma Vidya, Gutta Sridevi, Dandibhotla Teja Santosh
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	neural network softmax function and extraction Mathematics QA1-939 Probabilities. Mathematical statistics QA273-280
Zdroj:	Ratio Mathematica, Vol 44, Iss 0, Pp 213-221 (2022)
Druh dokumentu:	article
ISSN:	1592-7415 2282-8214
DOI:	10.23755/rm.v44i0.909
Popis:	The quality of the product is measured based on the opinions gathered from product reviews expressed on a product. Opinion mining deals with extracting the features or aspects from the reviews expressed by the users. Specifically, this model uses a deep convolutional neural network with three channels of input: a semantic word embedding channel that encodes the semantic content of the word, a part of speech tagging channel for sequential labelling and domain embedding channel for domain specific embeddings which is pooled and processed with a Softmax function. This model uses three input channels for aspect extraction. Experiments are conducted on amazon review dataset. This model achieved better results
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/3940d93f791043e6b7ece2fdda37c292 Zobrazit plný text záznamu View record in DOAJ