Machine-Learning-Based Approaches for Multi-Level Sentiment Analysis of Romanian Reviews

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AYNFNF36H" target="_blank" >RIV/00216208:11320/25:YNFNF36H - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184722366&doi=10.3390%2fmath12030456&partnerID=40&md5=126fe8050a10886fa9d1764b073f7fca" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184722366&doi=10.3390%2fmath12030456&partnerID=40&md5=126fe8050a10886fa9d1764b073f7fca</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/math12030456" target="_blank" >10.3390/math12030456</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine-Learning-Based Approaches for Multi-Level Sentiment Analysis of Romanian Reviews

Popis výsledku v původním jazyce

Sentiment analysis has increasingly gained significance in commercial settings, driven by the rising impact of reviews on purchase decision-making in recent years. This research conducts a thorough examination of the suitability of machine learning and deep learning approaches for sentiment analysis, using Romanian reviews as a case study, with the aim of gaining insights into their practical utility. A comprehensive, multi-level analysis is performed, covering the document, sentence, and aspect levels. The main contributions of the paper refer to the in-depth exploration of multiple sentiment analysis models at three different textual levels and the subsequent improvements brought with respect to these standard models. Additionally, a balanced dataset of Romanian reviews from twelve product categories is introduced. The results indicate that, at the document level, supervised deep learning techniques yield the best outcomes (specifically, a convolutional neural network model that obtains an AUC value of 0.93 for binary classification and a weighted average F1-score of 0.77 in a multi-class setting with 5 target classes), albeit with increased resource consumption. Favorable results are achieved at the sentence level, as well, despite the heightened complexity of sentiment identification. In this case, the best-performing model is logistic regression, for which a weighted average F1-score of 0.77 is obtained in a multi-class polarity classification task with three classes. Finally, at the aspect level, promising outcomes are observed in both aspect term extraction and aspect category detection tasks, in the form of coherent and easily interpretable word clusters, encouraging further exploration in the context of aspect-based sentiment analysis for the Romanian language. © 2024 by the authors.

Název v anglickém jazyce

Machine-Learning-Based Approaches for Multi-Level Sentiment Analysis of Romanian Reviews

Popis výsledku anglicky

Sentiment analysis has increasingly gained significance in commercial settings, driven by the rising impact of reviews on purchase decision-making in recent years. This research conducts a thorough examination of the suitability of machine learning and deep learning approaches for sentiment analysis, using Romanian reviews as a case study, with the aim of gaining insights into their practical utility. A comprehensive, multi-level analysis is performed, covering the document, sentence, and aspect levels. The main contributions of the paper refer to the in-depth exploration of multiple sentiment analysis models at three different textual levels and the subsequent improvements brought with respect to these standard models. Additionally, a balanced dataset of Romanian reviews from twelve product categories is introduced. The results indicate that, at the document level, supervised deep learning techniques yield the best outcomes (specifically, a convolutional neural network model that obtains an AUC value of 0.93 for binary classification and a weighted average F1-score of 0.77 in a multi-class setting with 5 target classes), albeit with increased resource consumption. Favorable results are achieved at the sentence level, as well, despite the heightened complexity of sentiment identification. In this case, the best-performing model is logistic regression, for which a weighted average F1-score of 0.77 is obtained in a multi-class polarity classification task with three classes. Finally, at the aspect level, promising outcomes are observed in both aspect term extraction and aspect category detection tasks, in the form of coherent and easily interpretable word clusters, encouraging further exploration in the context of aspect-based sentiment analysis for the Romanian language. © 2024 by the authors.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Mathematics

ISSN

2227-7390

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

36

Strana od-do

1-36

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85184722366