---
title: Sentiment Difficulty in ABSA
url: https://www.ml-quant.com/papers/repec/gam-jmathe-v-11-y-2023-i-22-p-4647-d-1280114/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:gam:jmathe:v:11:y:2023:i:22:p:4647-:d:1280114
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2227-7390%2F11%2F22%2F4647%2Fpdf%3Bh%3Drepec%3Agam%3Ajmathe%3Av%3A11%3Ay%3A2023%3Ai%3A22%3Ap%3A4647-%3Ad%3A1280114
featured: 2023-11-29
citations: unknown
topic: LLMs & Text
---


# Sentiment Difficulty in ABSA

A study investigates sentence difficulty in aspect-based sentiment analysis, using different learning models and text representations, and identifies the hardest sentences using a mix of classifiers.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2227-7390%2F11%2F22%2F4647%2Fpdf%3Bh%3Drepec%3Agam%3Ajmathe%3Av%3A11%3Ay%3A2023%3Ai%3A22%3Ap%3A4647-%3Ad%3A1280114
- Identifier: RePEc:gam:jmathe:v:11:y:2023:i:22:p:4647-:d:1280114
- Released: 2023-11-29
- First featured: Quant Letter No. 27 (2023-11-29): https://www.ml-quant.com/issues/2023-11-29/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: LLMs & Text

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