Neural networks with emotion associations, topic modeling and supervised term weighting for sentiment analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F21%3A39917730" target="_blank" >RIV/00216275:25410/21:39917730 - isvavai.cz</a>

Result on the web

<a href="https://www.worldscientific.com/doi/abs/10.1142/S0129065721500131" target="_blank" >https://www.worldscientific.com/doi/abs/10.1142/S0129065721500131</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1142/S0129065721500131" target="_blank" >10.1142/S0129065721500131</a>

Result language

angličtina

Original language name

Neural networks with emotion associations, topic modeling and supervised term weighting for sentiment analysis

Original language description

Automated sentiment analysis is becoming increasingly recognized due to the growing importance of social media and e-commerce platform review websites. Deep neural networks outperform traditional lexicon-based and machine learning methods by effectively exploiting contextual word embeddings to generate dense document representation. However, this representation model is not fully adequate to capture topical semantics and the sentiment polarity of words. To overcome these problems, a novel sentiment analysis model is proposed that utilizes richer document representations of word-emotion associations and topic models, which is the main computational novelty of this study. The sentiment analysis model integrates word embeddings with lexicon-based sentiment and emotion indicators, including negations and emoticons, and to further improve its performance, a topic modeling component is utilized together with a bag-of-words model based on a supervised term weighting scheme. The effectiveness of the proposed model is evaluated using large datasets of Amazon product reviews and hotel reviews. Experimental results prove that the proposed document representation is valid for the sentiment analysis of product and hotel reviews, irrespective of their class imbalance. The results also show that the proposed model improves on existing machine learning methods.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

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

2021

Confidentiality

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

Name of the periodical

International Journal of Neural Systems

ISSN

0129-0657

e-ISSN

—

Volume of the periodical

31

Issue of the periodical within the volume

10

Country of publishing house

SG - SINGAPORE

Number of pages

18

Pages from-to

2150013

UT code for WoS article

000696596800004

EID of the result in the Scopus database

2-s2.0-85100784177