Aspect-Based Sentiment Analysis for Slovene Texts: Models, Lexicons, and Embeddings

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A9ELXC6VL" target="_blank" >RIV/00216208:11320/25:9ELXC6VL - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192262138&doi=10.1109%2fIATMSI60426.2024.10503382&partnerID=40&md5=7b9e06c82075cf4b53ca0fe16b9d5aa7" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192262138&doi=10.1109%2fIATMSI60426.2024.10503382&partnerID=40&md5=7b9e06c82075cf4b53ca0fe16b9d5aa7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IATMSI60426.2024.10503382" target="_blank" >10.1109/IATMSI60426.2024.10503382</a>

Result language

angličtina

Original language name

Aspect-Based Sentiment Analysis for Slovene Texts: Models, Lexicons, and Embeddings

Original language description

When performing sentiment analysis on texts mentioning multiple entities, the sentiment towards each of them is not necessarily the same, and it is essential to determine what sentiment applies to each entity separately. In this paper, we study aspect-based sentiment analysis approaches applied to texts in Slovene. We implement three models. In the first model, we use a sentiment lexicon to determine the sentiment of words close to an entity in the same sentence and document and use those as features for a random forest classifier. In the second model, we add a neural model for dependency parsing to the pipeline and construct features based on words close in a sentence's dependency tree instead of sequentially. The third model uses BERT embeddings with a neural classifier to construct embeddings. We evaluate the approaches on the SentiCoref 1.0 corpus of Slovene texts for aspect-based sentiment analysis. © 2024 IEEE.

Czech name

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Czech description

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Type

D - Article in proceedings

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

Article name in the collection

IEEE Int. Conf. Interdiscip. Approaches Technol. Manag. Soc. Innov., IATMSI

ISBN

979-835036052-3

ISSN

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e-ISSN

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Number of pages

6

Pages from-to

1-6

Publisher name

Institute of Electrical and Electronics Engineers Inc.

Place of publication

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Event location

Gwalior

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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