@inproceedings{schimmenti-etal-2025-old,
title = "Old Reviews, New Aspects: Aspect Based Sentiment Analysis and Entity Typing for Book Reviews with {LLM}s",
author = "Schimmenti, Andrea and
De Giorgis, Stefano and
Vitali, Fabio and
van Erp, Marieke",
editor = "Alam, Mehwish and
Tchechmedjiev, Andon and
Gracia, Jorge and
Gromann, Dagmar and
di Buono, Maria Pia and
Monti, Johanna and
Ionov, Maxim",
booktitle = "Proceedings of the 5th Conference on Language, Data and Knowledge",
month = sep,
year = "2025",
address = "Naples, Italy",
publisher = "Unior Press",
url = "https://aclanthology.org/2025.ldk-1.27/",
pages = "266--276",
ISBN = "978-88-6719-333-2",
abstract = "This paper faces the problem of the limited availability of datasets for Aspect-Based Sentiment Analysis (ABSA) in the Cultural Heritage domain. Currently, the main datasets for ABSA are product or restaurant reviews. We expand this to book reviews. Our methodology employs an LLM to maintain domain relevance while preserving the linguistic authenticity and natural variations found in genuine reviews. Entity types are annotated through the tool Text2AMR2FRED and evaluated manually. Additionally, we finetuned Llama 3.1 8B as a baseline model that not only performs ABSA, but also performs Entity Typing (ET) with a set of classes from DOLCE foundational ontology, enabling precise categorization of target aspects within book reviews. We present three key contributions as a step forward expanding ABSA: 1) a semi-synthetic set of book reviews, 2) an evaluation of Llama-3-1-Instruct 8B on the ABSA task, and 3) a fine-tuned version of Llama-3-1-Instruct 8B for ABSA."
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%0 Conference Proceedings
%T Old Reviews, New Aspects: Aspect Based Sentiment Analysis and Entity Typing for Book Reviews with LLMs
%A Schimmenti, Andrea
%A De Giorgis, Stefano
%A Vitali, Fabio
%A van Erp, Marieke
%Y Alam, Mehwish
%Y Tchechmedjiev, Andon
%Y Gracia, Jorge
%Y Gromann, Dagmar
%Y di Buono, Maria Pia
%Y Monti, Johanna
%Y Ionov, Maxim
%S Proceedings of the 5th Conference on Language, Data and Knowledge
%D 2025
%8 September
%I Unior Press
%C Naples, Italy
%@ 978-88-6719-333-2
%F schimmenti-etal-2025-old
%X This paper faces the problem of the limited availability of datasets for Aspect-Based Sentiment Analysis (ABSA) in the Cultural Heritage domain. Currently, the main datasets for ABSA are product or restaurant reviews. We expand this to book reviews. Our methodology employs an LLM to maintain domain relevance while preserving the linguistic authenticity and natural variations found in genuine reviews. Entity types are annotated through the tool Text2AMR2FRED and evaluated manually. Additionally, we finetuned Llama 3.1 8B as a baseline model that not only performs ABSA, but also performs Entity Typing (ET) with a set of classes from DOLCE foundational ontology, enabling precise categorization of target aspects within book reviews. We present three key contributions as a step forward expanding ABSA: 1) a semi-synthetic set of book reviews, 2) an evaluation of Llama-3-1-Instruct 8B on the ABSA task, and 3) a fine-tuned version of Llama-3-1-Instruct 8B for ABSA.
%U https://aclanthology.org/2025.ldk-1.27/
%P 266-276
Markdown (Informal)
[Old Reviews, New Aspects: Aspect Based Sentiment Analysis and Entity Typing for Book Reviews with LLMs](https://aclanthology.org/2025.ldk-1.27/) (Schimmenti et al., LDK 2025)
ACL