# Explainable Sentiment Analysis with DeepSeek-R1: Performance, Efficiency, and Few-Shot Learning

arXiv: https://arxiv.org/abs/2503.11655 · HTML: https://mlsanity.com/paper/2503.11655/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 8 of 8)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Sentiment Analysis in the Era of Large Language Models: A Reality Check](https://mlsanity.com/paper/2305.15005/) (2023)
- [Reasoning Implicit Sentiment with Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.11255/) (2023)
- [Local Interpretations for Explainable Natural Language Processing: A Survey](https://mlsanity.com/paper/2103.11072/) (2021)
- [A Wide Evaluation of ChatGPT on Affective Computing Tasks](https://mlsanity.com/paper/2308.13911/) (2023)
- [Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns](https://mlsanity.com/paper/2404.14812/) (2024)

## Built on by (most cited first, 5 of 5)

- [Task Complexity Matters: An Empirical Study of Reasoning in LLMs for Sentiment Analysis](https://mlsanity.com/paper/2602.24060/) (2026)
- [Learning-to-Context Slope: Evaluating In-Context Learning Effectiveness Beyond Performance Illusions](https://mlsanity.com/paper/2506.23146/) (2025)
- [Fine-tuning of lightweight large language models for sentiment classification on heterogeneous financial textual data](https://mlsanity.com/paper/2512.00946/) (2025)
- [A Hormone-inspired Emotion Layer for Transformer language models (HELT)](https://mlsanity.com/paper/2605.13858/) (2026)
- [F2Agent: Financial Fusion of Agentic Intelligence for Multimodal Trading](https://mlsanity.com/paper/2608.05668/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have transformed sentiment analysis, yet balancing accuracy, efficiency, and explainability remains a critical challenge. This study presents the first comprehensive evaluation of DeepSeek-R1--an open-source reasoning model--against OpenAI's GPT-4o and GPT-4o-mini. We test the full 671B model and its distilled variants, systematically documenting few-shot learning curves. Our experiments show DeepSeek-R1 achieves a 91.39% F1 score on 5-class sentiment and 99.31% accuracy on binary tasks with just 5 shots, an eightfold improvement in few-shot efficiency over GPT-4o. Architecture-specific distillation effects emerge, where a 32B Qwen2.5-based model outperforms the 70B Llama-based variant by 6.69 percentage points. While its reasoning process reduces throughput, DeepSeek-R1 offers superior explainability via transparent, step-by-step traces, establishing it as a powerful, interpretable open-source alternative.