Evaluating the Sentiment Analysis from Auto-Generated Summary Text Using IndoBERT Fine-Tuning Model in Indonesian News Text

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3ATEWHUZH8" target="_blank" >RIV/00216208:11320/25:TEWHUZH8 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/abstract/document/10402345" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10402345</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CICN59264.2023.10402345" target="_blank" >10.1109/CICN59264.2023.10402345</a>

Result language

angličtina

Original language name

Evaluating the Sentiment Analysis from Auto-Generated Summary Text Using IndoBERT Fine-Tuning Model in Indonesian News Text

Original language description

Recently, online news has replaced conventional magazines and physical newspapers because of their intuitiveness and timeliness. News sites provide a comprehensive overview of important current events, serving as a valuable source for learning about a country's latest social, political, and economic issues. The government utilizes news channels to get an overview of the specific problems with sentiment analysis. However, the current system only reads news headlines to determine sentiment, so it does not thoroughly measure the opinion in the news content. This situation causes errors in sentiment reading, which should be negatively interpreted as positive or vice versa. This research tests the auto-generated summary text using the IndoBERT fine-tuning model to label the sentiment of news text. This research shows that fine-tuning IndoBERT using the human-made summaries dataset achieves the optimal outcome, with an F1-score of 75% compared to the 65% F1-Score of the auto-generated summary testing dataset. This study shows that the sentiment analysis prediction using the human-made summary dataset scores better than the sentiment analysis resulting from the Autogenerated summary testing dataset.

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

2023

Confidentiality

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

Article name in the collection

2023 IEEE 15th International Conference on Computational Intelligence and Communication Networks (CICN)

ISBN

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ISSN

2472-7555

e-ISSN

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Number of pages

8

Pages from-to

822-829

Publisher name

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Place of publication

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Event location

Bangkok, Thailand

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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