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

TL;DR

This paper introduces ACOS_LLM, a novel aspect-based sentiment analysis model for tourism that leverages large language models, auxiliary knowledge, and positional information to improve extraction accuracy and reduce errors.

Contribution

The paper presents a new model combining large language models, knowledge generation, and positional encoding for improved aspect-based sentiment analysis in tourism.

Findings

Achieved 7.49% F1 improvement on tourism dataset

Enhanced extraction accuracy on Rest15 and Rest16 datasets

Demonstrated effectiveness of auxiliary knowledge and positional info

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.