Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T23:39:18.763899Z
Paper Citation Record · LEDGER
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models
As of 18 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2505.04135.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
12 of 12 outbound references displayed
- verified exact2
- verified fuzzy8
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation f351f523-28b1-4f71-93e9-707b061d1b2e · outbound
This paper cites Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales,.
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:39:19.066580Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 16ed25f6-6ad3-441c-be69-e833d448cfc1 · outbound
This paper cites Chain of thought prompting elicits reasoning in large language models,.
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models Chain of thought prompting elicits reasoning in large language models,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:39:19.051517Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b39a5c3e-99e8-46c8-b05f-d17c89a02726 · outbound
This paper cites How do users like this feature? a fine-grained sentiment analy- sis of app reviews,.
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models How do users like this feature? a fine-grained sentiment analy- sis of app reviews,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:39:19.036430Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 82913f21-b7d8-434e-ab90-b3e134a97f61 · outbound
This paper cites Scare: The sentiment corpus of app reviews with fine-grained annotations,.
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models Scare: The sentiment corpus of app reviews with fine-grained annotations,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:39:19.020813Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 8c5126cf-4516-478a-a6ac-d751e3e59165 · outbound
This paper cites Senticnet 5: Discovering conceptual primi- tives for advancing sentiment analysis,.
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models Senticnet 5: Discovering conceptual primi- tives for advancing sentiment analysis,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:39:19.004968Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9f852ed2-5203-4d76-bc81-3102b3be129f · outbound
This paper cites Language models are few-shot learners,.
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models Language models are few-shot learners,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:39:18.989278Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ba283a9e-f19c-4f14-a4a0-3390c3d5838d · outbound
This paper cites How Effectively Do LLMs Extract Feature-Sentiment Pairs from App Reviews?.
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models How Effectively Do LLMs Extract Feature-Sentiment Pairs from App Reviews?
Reference 7
- Resolution
- verified exact
- local_arxiv, observed 2026-08-15T23:39:18.943712Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 17c8ee83-09d5-4712-821b-f4efd589334d · outbound
This paper cites Large language models are zero- shot reasoners,.
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models Large language models are zero- shot reasoners,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:39:18.973741Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a8c8e218-01cc-4c5b-a33a-a8e72aeeb9dc · outbound
This paper cites Controllable Data Augmentation for Few-Shot Text Mining with Chain-of-Thought Attribute Manipulation.
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models Controllable Data Augmentation for Few-Shot Text Mining with Chain-of-Thought Attribute Manipulation
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:39:18.750067Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 07b81bad-6254-4f1e-9702-21b804cdb7fc · outbound
This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:39:18.754496Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 757b0266-7439-4138-ab5f-95269a23e9ea · outbound
This paper cites Multimodal pear chain-of-thought reasoning for multimodal senti- ment analysis,.
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models Multimodal pear chain-of-thought reasoning for multimodal senti- ment analysis,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:39:18.959179Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b55ce896-2a9c-42b9-8dbb-4b9ecdaade41 · outbound
This paper cites Evaluating chatgpt-4 and machine learning models for sentiment analysis on a multi-script moroccan arabic corpus,.
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models Evaluating chatgpt-4 and machine learning models for sentiment analysis on a multi-script moroccan arabic corpus,
Reference 12
- Resolution
- verified exact
- raw_fallback, observed 2026-08-15T23:39:18.887725Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
No inbound Pith citation observations are available.