Sustainable sentiment analysis on E-commerce platforms using a weighted parallel hybrid deep learning approach for smart cities applications
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10256461" target="_blank" >RIV/61989100:27240/24:10256461 - isvavai.cz</a>
Result on the web
<a href="https://www.nature.com/articles/s41598-024-78318-1" target="_blank" >https://www.nature.com/articles/s41598-024-78318-1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41598-024-78318-1" target="_blank" >10.1038/s41598-024-78318-1</a>
Alternative languages
Result language
angličtina
Original language name
Sustainable sentiment analysis on E-commerce platforms using a weighted parallel hybrid deep learning approach for smart cities applications
Original language description
Sentiment analysis of several user evaluations on e-commerce platforms can be used to increase customer happiness. This method automatically extracts and identifies subjective data from product evaluations using natural language processing and machine learning methods. These statistics may eventually reveal information on the favourable, neutral, or negative attitudes of the consumer base. Due to its capacity to grasp the complex links between words and phrases in reviews as well as the emotions they imply, deep learning (DL) is very useful for SA tasks. A unique approach termed Weighted Parallel Hybrid Deep Learning-based Sentiment Analysis on E-Commerce Product Reviews is introduced by the proposed system. Accurately distinguishing between distinct sentiments found in online store reviews is the aim of the system technique. Additional data pre-processing processes are implemented within the system architecture to guarantee compatibility.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Scientific Reports
ISSN
2045-2322
e-ISSN
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Volume of the periodical
14
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
20
Pages from-to
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UT code for WoS article
001349432200067
EID of the result in the Scopus database
2-s2.0-85208493917