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ARXIV:2603.11254 · NLP · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.11254NLPSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
This research explores the use of LLMs for sentiment analysis of Persian poetry, revealing insights into emotional expression and poetic structure.
Opportunity summary
Pain This research explores the use of LLMs for sentiment analysis of Persian poetry, revealing insights into emotional expression and poetic structure.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
This research explores the use of LLMs for sentiment analysis of Persian poetry, revealing insights into emotional expression and poetic structure. These language models open a significant opportunity in analyzing the literature and more…
Recent advancements of the Artificial Intelligence (AI) have led to the development of large language models (LLMs) that are capable of understanding, analysing, and creating textual data. These language models open a significant opportunity…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Furthermore, the results of our sentiment analysis revealed that in general, Rumi's poems express happier sentiments compared to Parvin E'tesami's poems.
NLP moved forward this cycle; last verified April 2026. Public score 2.0/10.
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This research explores the use of LLMs for sentiment analysis of Persian poetry, revealing insights into emotional expression and poetic structure.
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10.48550/arXiv.2603.11254This research explores the use of LLMs for sentiment analysis of Persian poetry, revealing insights into emotional expression and poetic structure.
Abstract
Recent advancements of the Artificial Intelligence (AI) have led to the development of large language models (LLMs) that are capable of understanding, analysing, and creating textual data. These language models open a significant opportunity in analyzing the literature and more specifically poetry. In the present work, we employ multiple Bidirectional encoder representations from transformers (BERT) and Generative Pre-trained Transformer (GPT) based language models to analyze the works of two prominent Persian poets: Jalal al-Din Muhammad Rumi (Rumi) and Parvin E'tesami. The main objective of this research is to investigate the capability of the modern language models in grasping complexities of the Persian poetry and explore potential correlations between the poems' sentiment and their meters. Our findings in this study indicates that GPT4o language model can reliably be used in analysis of Persian poetry. Furthermore, the results of our sentiment analysis revealed that in general, Rumi's poems express happier sentiments compared to Parvin E'tesami's poems. Furthermore, comparing the utilization of poetic meters highlighted Rumi's poems superiority in using meters to express a wider variety of sentiments. These findings are significant as they confirm that LLMs can be effectively applied in conducting computer-based semantic studies, where human interpretations are not required, and thereby significantly reducing potential biases in the analysis.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 2.0
PROBLEM
This research explores the use of LLMs for sentiment analysis of Persian poetry, revealing insights into emotional expression and poetic structure. These language models open a significant opportunity in analyzing the literature and more specifically poetry.
METHOD
Recent advancements of the Artificial Intelligence (AI) have led to the development of large language models (LLMs) that are capable of understanding, analysing, and creating textual data. These language models open a significant opportunity in analyzing the literature and more...
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Furthermore, the results of our sentiment analysis revealed that in general, Rumi's poems express happier sentiments compared to Parvin E'tesami's poems.
WHY NOW
NLP moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed public claims while anchored extraction refreshes.
This research explores the use of LLMs for sentiment analysis of Persian poetry, revealing insights into emotional expression and poetic structure. These language models open a significant opportunity in analyzing the literature and more specifically poetry.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Recent advancements of the Artificial Intelligence (AI) have led to the development of large language models (LLMs) that are capable of understanding, analysing, and creating textual data. These language models open a significant opportunity in analyzing the literature and more specifically poetry.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Furthermore, the results of our sentiment analysis revealed that in general, Rumi's poems express happier sentiments compared to Parvin E'tesami's poems.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
NLP moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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This research explores the use of LLMs for sentiment analysis of Persian poetry, revealing insights into emotional expression and poetic structure.
Segment
NLP
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No public code link in the paper record yet
Commercial read
2.0/10 public viability
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reason
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proof status
unverified
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confidence low
next verification path
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passport absent
stale
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Artifact maturity
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stale
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Technical feasibility
partial
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Gaps
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0 references, 0 sources, 17% evidence coverage.
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Buyer clarity
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missing
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Write integration checklist from prototype path and target workflow.
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