Sustainable sentiment analysis on E-commerce platforms using a weighted parallel hybrid deep learning approach for smart cities applications

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Sustainable sentiment analysis on E-commerce platforms using a weighted parallel hybrid deep learning approach for smart cities applications

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Sustainable sentiment analysis on E-commerce platforms using a weighted parallel hybrid deep learning approach for smart cities applications

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Scientific Reports

ISSN

2045-2322

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

—

Kód UT WoS článku

001349432200067

EID výsledku v databázi Scopus

2-s2.0-85208493917