Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)

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Leveraging Adapter Configurations for Sentiment Classification in Financial Text Analysis

Authors
Sachin Babu Antony1, *, Pravallika Papineni1
1Indepenedent Researcher, Salford, UK
*Corresponding author. Email: sachinbabuantony@gmail.com
Corresponding Author
Sachin Babu Antony
Available Online 17 July 2025.
DOI
10.2991/978-94-6463-787-8_51How to use a DOI?
Keywords
Adapter Configurations; XG-Boost; Receiver Operating Characteristic (ROC); Captum; Financial Analysis
Abstract

This review investigates the use of connector design sin models for opinion grouping within financial text data, with a specific focus on Securities and Exchange Commission (SEC) filings and analyst reports. We evaluate several connector strategies, including LoRAConfig, UniPELTConfig, and MAMConfig, to identify optimal designs that balance performance and computational efficiency. The experiments follow a structured analysis approach, comparing various models and configurations to determine the best solution for analyzing financial reports. Our finding semphasize the importance of using Receiver Operating Characteristic (ROC) curves for comprehensive model evaluation. They reveal that traditional benchmarks, such as analyst forecasts and XG-Boost, often underperform compared to random predictions. By employing feature attribution through Captum, we provide insights into the impact of specific textual features on model predictions. Ultimately, this research highlights the potential of connector-based approaches in enhancing sentiment analysis within the financial domain, paving the way for more accurate automated financial assessments.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)
Series
Advances in Intelligent Systems Research
Publication Date
17 July 2025
ISBN
978-94-6463-787-8
ISSN
1951-6851
DOI
10.2991/978-94-6463-787-8_51How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Sachin Babu Antony
AU  - Pravallika Papineni
PY  - 2025
DA  - 2025/07/17
TI  - Leveraging Adapter Configurations for Sentiment Classification in Financial Text Analysis
BT  - Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)
PB  - Atlantis Press
SP  - 671
EP  - 685
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-787-8_51
DO  - 10.2991/978-94-6463-787-8_51
ID  - Antony2025
ER  -