Related papers: Enhancing Aspect-based Sentiment Analysis in Tourism Using Large Language Models and Positional Information

Enhancing Aspect-based Sentiment Analysis in Tourism Using Large Language Models and Positional Information

URL: http://arxiv.org/abs/2409.14997v1
Date: Mon, 23 Sep 2024 13:19:17 GMT
Title: Enhancing Aspect-based Sentiment Analysis in Tourism Using Large Language Models and Positional Information
Authors: Chun Xu, Mengmeng Wang, Yan Ren, Shaolin Zhu,
Abstract summary: This paper proposes an aspect-based sentiment analysis model, ACOS_LLM, for Aspect-Category--Sentiment Quadruple Extraction (ACOSQE) The model comprises two key stages: auxiliary knowledge generation and ACOSQE. Results demonstrate the model's superior performance, with an F1 improvement of 7.49% compared to other models on the tourism dataset.
Score: 14.871979025512669
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Aspect-Based Sentiment Analysis (ABSA) in tourism plays a significant role in understanding tourists' evaluations of specific aspects of attractions, which is crucial for driving innovation and development in the tourism industry. However, traditional pipeline models are afflicted by issues such as error propagation and incomplete extraction of sentiment elements. To alleviate this issue, this paper proposes an aspect-based sentiment analysis model, ACOS_LLM, for Aspect-Category-Opinion-Sentiment Quadruple Extraction (ACOSQE). The model comprises two key stages: auxiliary knowledge generation and ACOSQE. Firstly, Adalora is used to fine-tune large language models for generating high-quality auxiliary knowledge. To enhance model efficiency, Sparsegpt is utilized to compress the fine-tuned model to 50% sparsity. Subsequently, Positional information and sequence modeling are employed to achieve the ACOSQE task, with auxiliary knowledge and the original text as inputs. Experiments are conducted on both self-created tourism datasets and publicly available datasets, Rest15 and Rest16. Results demonstrate the model's superior performance, with an F1 improvement of 7.49% compared to other models on the tourism dataset. Additionally, there is an F1 improvement of 0.05% and 1.06% on the Rest15 and Rest16 datasets, respectively.

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