Opinion mining of consumer reviews using deep neural networks with word-sentiment associations

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F20%3A39916681" target="_blank" >RIV/00216275:25410/20:39916681 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-030-49161-1_35" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-49161-1_35</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-49161-1_35" target="_blank" >10.1007/978-3-030-49161-1_35</a>

Result language

angličtina

Original language name

Opinion mining of consumer reviews using deep neural networks with word-sentiment associations

Original language description

Automated opinion mining of consumer reviews is becoming increasingly important due to the rising influence of reviews on online retail shopping. Existing approaches to automated opinion classification rely either on sentiment lexicons or supervised machine learning. Deep neural networks perform this classification task particularly well by utilizing dense document representation in terms of word embeddings. However, this representation model does not consider the sentiment polarity or sentiment intensity of the words. To overcome this problem, we propose a novel model of deep neural network with word-sentiment associations. This model produces richer document representation that incorporates both word context and word sentiment. Specifically, our model utilizes pre-trained word embeddings and lexicon-based sentiment indicators to provide inputs to a deep feed-forward neural network. To verify the effectiveness of the proposed model, a benchmark dataset of Amazon reviews is used. Our results strongly support integrated document representation, which shows that the proposed model outperforms other existing machine learning approaches to opinion mining of consumer reviews.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA19-15498S" target="_blank" >GA19-15498S: Modelling emotions in verbal and nonverbal managerial communication to predict corporate financial risk</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

IFIP Advances in Information and Communication Technology. Vol. 583

ISBN

978-3-030-49160-4

ISSN

1868-4238

e-ISSN

—

Number of pages

11

Pages from-to

419-429

Publisher name

Springer

Place of publication

Heidelberg

Event location

Neos Marmaras

Event date

Jun 5, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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