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.

Related papers

• Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models [75.58140912100318]
  We introduce an efficient large language model specialized for the system domain, empowered by a novel architecture including DiffQKV attention. We conduct experiments that demonstrate the model's varying sensitivity to the compression of K and V components, leading to the development of differentially compressed KV. We introduce the first comprehensive benchmark AIMicius, where Sigma demonstrates remarkable performance across all tasks, significantly outperforming GPT-4 with an absolute improvement up to 52.5%.
  arXiv  Detail & Related papers  (2025-01-23T12:58:14Z)
• Explainable AI-aided Feature Selection and Model Reduction for DRL-based V2X Resource Allocation [18.49800990388549]
  Artificial intelligence (AI) is expected to significantly enhance radio resource management (RRM) in sixth-generation (6G) networks. The lack of explainability in complex deep learning (DL) models poses a challenge for practical implementation. This paper proposes a novel explainable AI (XAI)-based framework for feature selection and model complexity reduction.
  arXiv  Detail & Related papers  (2025-01-23T10:55:38Z)
• SMPLest-X: Ultimate Scaling for Expressive Human Pose and Shape Estimation [81.36747103102459]
  Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion capture with numerous applications. Current state-of-the-art methods focus on training innovative architectural designs on confined datasets. We investigate the impact of scaling up EHPS towards a family of generalist foundation models.
  arXiv  Detail & Related papers  (2025-01-16T18:59:46Z)
• MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models [7.422346909538787]
  We introduce MapEval, a benchmark designed to assess diverse and complex map-based user queries with geo-spatial reasoning. MapEval consists of 700 unique multiple-choice questions about locations across 180 cities and 54 countries. Our detailed analyses provide insights into the strengths and weaknesses of current models, though all models still fall short of human performance by more than 20% on average. This gap highlights MapEval's critical role in advancing general-purpose foundation models with stronger geo-spatial understanding.
  arXiv  Detail & Related papers  (2024-12-31T07:20:32Z)
• Emotion Classification from Multi-Channel EEG Signals Using HiSTN: A Hierarchical Graph-based Spatial-Temporal Approach [0.0]
  This study introduces a parameter-efficient network for emotion classification. The network incorporates a graph hierarchy constructed from bottom-up at various abstraction levels. It achieves mean F1 scores of 96.82% (valence) and 95.62% (arousal) in subject-dependent tests.
  arXiv  Detail & Related papers  (2024-08-09T12:32:12Z)
• Automated Root Causing of Cloud Incidents using In-Context Learning with GPT-4 [23.856839017006386]
  Root Cause Analysis (RCA) plays a pivotal role in the incident diagnosis process for cloud services. GPT-4 model's immense size presents challenges when trying to fine-tune it on user data. We propose an in-context learning approach for automated root causing, which eliminates the need for fine-tuning.
  arXiv  Detail & Related papers  (2024-01-24T21:02:07Z)
• Data-Centric Long-Tailed Image Recognition [49.90107582624604]
  Long-tail models exhibit a strong demand for high-quality data. Data-centric approaches aim to enhance both the quantity and quality of data to improve model performance. There is currently a lack of research into the underlying mechanisms explaining the effectiveness of information augmentation.
  arXiv  Detail & Related papers  (2023-11-03T06:34:37Z)
• Scaling Data Generation in Vision-and-Language Navigation [116.95534559103788]
  We propose an effective paradigm for generating large-scale data for learning. We apply 1200+ photo-realistic environments from HM3D and Gibson datasets and synthesizes 4.9 million instruction trajectory pairs. Thanks to our large-scale dataset, the performance of an existing agent can be pushed up (+11% absolute with regard to previous SoTA) to a significantly new best of 80% single-run success rate on the R2R test split by simple imitation learning.
  arXiv  Detail & Related papers  (2023-07-28T16:03:28Z)
• GENEVA: Benchmarking Generalizability for Event Argument Extraction with Hundreds of Event Types and Argument Roles [77.05288144035056]
  Event Argument Extraction (EAE) has focused on improving model generalizability to cater to new events and domains. Standard benchmarking datasets like ACE and ERE cover less than 40 event types and 25 entity-centric argument roles.
  arXiv  Detail & Related papers  (2022-05-25T05:46:28Z)
• How Knowledge Graph and Attention Help? A Quantitative Analysis into Bag-level Relation Extraction [66.09605613944201]
  We quantitatively evaluate the effect of attention and Knowledge Graph on bag-level relation extraction (RE) We find that (1) higher attention accuracy may lead to worse performance as it may harm the model's ability to extract entity mention features; (2) the performance of attention is largely influenced by various noise distribution patterns; and (3) KG-enhanced attention indeed improves RE performance, while not through enhanced attention but by incorporating entity prior.
  arXiv  Detail & Related papers  (2021-07-26T09:38:28Z)
• Tourism Demand Forecasting: An Ensemble Deep Learning Approach [4.516340427736994]
  We use historical tourist arrival data, economic variable data and search intensity index (SII) data to forecast tourist arrivals in Beijing. Our proposed B-SAKE approach outperforms benchmark models in terms of level accuracy, directional accuracy and even statistical significance. Both bagging and stacked autoencoder can effectively alleviate the challenges brought by tourism big data and improve the forecasting performance of the models.
  arXiv  Detail & Related papers  (2020-02-19T02:23:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.