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Volume 16
Issue 7
10.3390/info16070562
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Machine Learning with Self-Assessment Manikin Valence Scale for Fine-Grained Sentiment Analysis

by
Lindung Parningotan Manik
1,2,*,
Harry Susianto
3,
Arawinda Dinakaramani
4,
Niken Pramanik
5 and
Totok Suhardijanto
5,†
1
Research Center for Data and Information Sciences, National Research and Innovation Agency, Bandung 40135, Indonesia
2
Faculty of Information Technology, Universitas Nusa Mandiri, Jakarta 13620, Indonesia
3
Faculty of Psychology, Universitas Indonesia, Depok 16424, Indonesia
4
Faculty of Computer Science, Universitas Indonesia, Depok 16424, Indonesia
5
Department of Linguistics, Faculty of Humanities, Universitas Indonesia, Depok 16424, Indonesia
*
Author to whom correspondence should be addressed.
†
The author passed away prior to the submission of this paper. This is one of his last works.
Information 2025, 16(7), 562; https://doi.org/10.3390/info16070562
Submission received: 29 April 2025 / Revised: 9 June 2025 / Accepted: 24 June 2025 / Published: 30 June 2025
Versions Notes

Abstract

Traditional sentiment analysis methods use lexicons or machine learning models to classify text as positive or negative. These approaches are unable to capture nuance or intensity in short or informal texts. We propose a novel method that uses the Self-Assessment Manikin (SAM) valence scale, which provides a continuous measurement of sentiment, ranging from extremely positive to extremely negative. We describe the development of a lexicon of emotion-laden words with SAM valence scales and investigate its application to fine-grained sentiment analysis. We also propose a lexicon-based polarity approach to complement textual features in machine learning models trained to predict a numerical sentiment label for a given text. This method is evaluated using a new dataset of short texts with sentiment labels based on expert ratings, which are predicted using various machine learning fusion mechanisms. The lexicon-based polarity method is found to provide improvements of 0.250, 0.999, and 0.261 in the mean squared error for classical machine learning, RNN, and transformer-based architectures, respectively.
Keywords: sentiment analysis; fine-grained sentiment; lexicon-based polarity; self-assessment manikin; valence) sentiment analysis; fine-grained sentiment; lexicon-based polarity; self-assessment manikin; valence)

Share and Cite

MDPI and ACS Style

Manik, L.P.; Susianto, H.; Dinakaramani, A.; Pramanik, N.; Suhardijanto, T. Machine Learning with Self-Assessment Manikin Valence Scale for Fine-Grained Sentiment Analysis. Information 2025, 16, 562. https://doi.org/10.3390/info16070562

AMA Style

Manik LP, Susianto H, Dinakaramani A, Pramanik N, Suhardijanto T. Machine Learning with Self-Assessment Manikin Valence Scale for Fine-Grained Sentiment Analysis. Information. 2025; 16(7):562. https://doi.org/10.3390/info16070562

Chicago/Turabian Style

Manik, Lindung Parningotan, Harry Susianto, Arawinda Dinakaramani, Niken Pramanik, and Totok Suhardijanto. 2025. "Machine Learning with Self-Assessment Manikin Valence Scale for Fine-Grained Sentiment Analysis" Information 16, no. 7: 562. https://doi.org/10.3390/info16070562

APA Style

Manik, L. P., Susianto, H., Dinakaramani, A., Pramanik, N., & Suhardijanto, T. (2025). Machine Learning with Self-Assessment Manikin Valence Scale for Fine-Grained Sentiment Analysis. Information, 16(7), 562. https://doi.org/10.3390/info16070562

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