Sentiment Analysis for Financial News Using RNN-LSTM Network

ICORIS : 2022 4th International Conference on Cybernetics and Intelligent System
March 4, 2023 by
Sentiment Analysis for Financial News Using RNN-LSTM Network
kelvin leonardi

Abstract


Identifying financial sector sentiment, primarily through the financial news, is crucial in financial investment decisions. Financial Sentiment Analysis (FSA) significantly affects the secondary market and provides a significant contribution. Long Short-Term Memory (LSTM) is a deep learning model, especially Recurrent Neural Network (RNN), a reasonably popular model designed for long-term​ constraints. Various methods have been proposed in previous studies, but the performance generated by the model in previous studies is still below 90%, so it has the opportunity to be improved. This study aims to present a sentiment analysis model by implementing the RNN-LSTM. The results showed that the model used for sentiment analysis of financial news reached 92.23% precision, 91.54% accuracy, 90.99% recall, and an f1 score of 91.61%. The model we built is helpful for understanding trends and opinions for making financial and other investment decisions.

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Kelvin Leonardi Kohsasih

Universitas Potensi Utama

B. Herawan Hayadi

Universitas Potensi Utama

Robet

STMIK TIME

Carles Juliandy

STMIK TIME

Octara Pribadi

STMIK TIME

Andi

STMIK TIME 

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Citation:

K. L. Kohsasih, B. H. Hayadi, Robet, C. Juliandy, O. Pribadi and Andi, "Sentiment Analysis for Financial News Using RNN-LSTM Network," 2022 4th International Conference on Cybernetics and Intelligent System (ICORIS), Prapat, Indonesia, 2022, pp. 1-6, doi: 10.1109/ICORIS56080.2022.10031595.

Publication:
2022 4th International Conference on Cybernetics and Intelligent System (ICORIS)
DOI:
10.1109/ICORIS56080.2022.10031595

Copyright:

Copyright (c) IEEE Xplore
Sentiment Analysis for Financial News Using RNN-LSTM Network
kelvin leonardi March 4, 2023
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Deep Lerning Financial News Long Short-Term Memory Recurrent Neural Network Sentiment Analysis
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