mlsanityData to 7 Oct 2026

Reassessing the Role of Chain-of-Thought in Sentiment Analysis: Insights and Limitations

arXiv 2501.08641 ↗First posted 15 Jan 2025cs.CL cs.AIcited by 3

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

The relationship between language and thought remains an unresolved philosophical issue. Existing viewpoints can be broadly categorized into two schools: one asserting their independence, and another arguing that language constrains thought. In the context of large language models, this debate raises a crucial question: Does a language model's grasp of semantic meaning depend on thought processes? To explore this issue, we investigate whether reasoning techniques can facilitate semantic understanding. Specifically, we conceptualize thought as reasoning, employ chain-of-thought prompting as a reasoning technique, and examine its impact on sentiment analysis tasks. The experiments show that chain-of-thought has a minimal impact on sentiment analysis tasks. Both the standard and chain-of-thought prompts focus on aspect terms rather than sentiment in the generated content. Furthermore, counterfactual experiments reveal that the model's handling of sentiment tasks primarily depends on information from demonstrations. The experimental results support the first viewpoint.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 10 of its 26 references that are in the map. First posted on arXiv 15 Jan 2025.

Builds on

10 of 26 references.

References · 10

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
  3. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,122
  4. 2024Gemma: Open Models Based on Gemini Research and Technologycited by 889
  5. 2023Larger language models do in-context learning differentlycited by 386
  6. 2021Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Trainingcited by 37
  7. 2023Towards Understanding In-Context Learning with Contrastive Demonstrations and Saliency Mapscited by 35
  8. 2024RVISA: Reasoning and Verification for Implicit Sentiment Analysiscited by 8
  9. 2024nicolay-r at SemEval-2024 Task 3: Using Flan-T5 for Reasoning Emotion Cause in Conversations with Chain-of-Thought on Emotion Statescited by 2
  10. 2024Word Order and World Knowledgecited by 1

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025LLaMAs Have Feelings Too: Unveiling Sentiment and Emotion Representations in LLaMA Models Through Probingcited by 14
  2. 2025ChatMotion: A Multimodal Multi-Agent for Human Motion Analysiscited by 13
  3. 2025SafeMT: Multi-turn Safety for Multimodal Language Modelscited by 4

Similar, not cited

Close in wording, no citation either way

  1. 2024Chain-of-Thought in Large Language Models: Decoding, Projection, and Activation
  2. 2026Does Verbose Chain-of-Thought Really Help? In-Distribution Evidence that Content, Not Length, Matters
  3. 2025Rethinking the Chain-of-Thought: The Roles of In-Context Learning and Pre-trained Priors
  4. 2023Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models
  5. 2022Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models
  6. 2026Dynamics Within Latent Chain-of-Thought: An Empirical Study of Causal Structure
  7. 2025Chain-of-Thought as a Lens: Evaluating Structured Reasoning Alignment between Human Preferences and Large Language Models
  8. 2024Larger Language Models Don't Care How You Think: Why Chain-of-Thought Prompting Fails in Subjective Tasks