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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

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

Project

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Continuities

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Publication year

2024

Confidentiality

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

Name of the periodical

Mathematics

ISSN

2227-7390

e-ISSN

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Volume of the periodical

12

Issue of the periodical within the volume

3

Country of publishing house

US - UNITED STATES

Number of pages

36

Pages from-to

1-36

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85184722366