Utilizing Large Language Models for Event Deconstruction to Enhance Multimodal Aspect-Based Sentiment Analysis

• URL: http://arxiv.org/abs/2410.14150v1
• Date: Fri, 18 Oct 2024 03:40:45 GMT
• Title: Utilizing Large Language Models for Event Deconstruction to Enhance Multimodal Aspect-Based Sentiment Analysis
• Authors: Xiaoyong Huang, Heli Sun, Qunshu Gao, Wenjie Huang, Ruichen Cao,
• Abstract summary: This paper introduces Large Language Models (LLMs) for event decomposition and proposes a reinforcement learning framework for Multimodal Aspect-based Sentiment Analysis (MABSA-RL) Experimental results show that MABSA-RL outperforms existing advanced methods on two benchmark datasets.
• Score: 2.1329326061804816
• License:
• Abstract: With the rapid development of the internet, the richness of User-Generated Contentcontinues to increase, making Multimodal Aspect-Based Sentiment Analysis (MABSA) a research hotspot. Existing studies have achieved certain results in MABSA, but they have not effectively addressed the analytical challenges in scenarios where multiple entities and sentiments coexist. This paper innovatively introduces Large Language Models (LLMs) for event decomposition and proposes a reinforcement learning framework for Multimodal Aspect-based Sentiment Analysis (MABSA-RL) framework. This framework decomposes the original text into a set of events using LLMs, reducing the complexity of analysis, introducing reinforcement learning to optimize model parameters. Experimental results show that MABSA-RL outperforms existing advanced methods on two benchmark datasets. This paper provides a new research perspective and method for multimodal aspect-level sentiment analysis.

Related papers

• An Empirical Analysis of Speech Self-Supervised Learning at Multiple Resolutions [0.3495246564946556]
  We present an initial analysis of layer-wise representations in multi-scale architectures, with a focus on Canonical Correlation Analysis (CCA) and Mutual Information (MI) We find that the improved performance on SUPERB tasks is primarily due to the auxiliary low-resolution loss rather than the downsampling itself. These findings challenge assumptions about the multi-scale nature of MR-HuBERT and motivate the importance of disentangling computational efficiency from learning better representations.
  arXiv  Detail & Related papers  (2024-10-31T14:09:05Z)
• Towards Robust Multimodal Sentiment Analysis with Incomplete Data [20.75292807497547]
  We present an innovative Language-dominated Noise-resistant Learning Network (LNLN) to achieve robust Multimodal Sentiment Analysis (MSA) LNLN features a dominant modality correction (DMC) module and dominant modality based multimodal learning (DMML) module, which enhances the model's robustness across various noise scenarios.
  arXiv  Detail & Related papers  (2024-09-30T07:14:31Z)
• A Survey on Multimodal Benchmarks: In the Era of Large AI Models [13.299775710527962]
  Multimodal Large Language Models (MLLMs) have brought substantial advancements in artificial intelligence. This survey systematically reviews 211 benchmarks that assess MLLMs across four core domains: understanding, reasoning, generation, and application.
  arXiv  Detail & Related papers  (2024-09-21T15:22:26Z)
• Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
  Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
  arXiv  Detail & Related papers  (2024-07-17T20:01:21Z)
• Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment [0.23020018305241333]
  This paper describes our approach to hierarchical multi-label detection of persuasion techniques in meme texts. The scope of the study encompasses enhancing model performance through innovative training techniques and data augmentation strategies.
  arXiv  Detail & Related papers  (2024-07-01T20:25:20Z)
• Explore In-Context Segmentation via Latent Diffusion Models [132.26274147026854]
  latent diffusion model (LDM) is an effective minimalist for in-context segmentation. We build a new and fair in-context segmentation benchmark that includes both image and video datasets.
  arXiv  Detail & Related papers  (2024-03-14T17:52:31Z)
• Model Composition for Multimodal Large Language Models [71.5729418523411]
  We propose a new paradigm through the model composition of existing MLLMs to create a new model that retains the modal understanding capabilities of each original model. Our basic implementation, NaiveMC, demonstrates the effectiveness of this paradigm by reusing modality encoders and merging LLM parameters.
  arXiv  Detail & Related papers  (2024-02-20T06:38:10Z)
• A Novel Energy based Model Mechanism for Multi-modal Aspect-Based Sentiment Analysis [85.77557381023617]
  We propose a novel framework called DQPSA for multi-modal sentiment analysis. PDQ module uses the prompt as both a visual query and a language query to extract prompt-aware visual information. EPE module models the boundaries pairing of the analysis target from the perspective of an Energy-based Model.
  arXiv  Detail & Related papers  (2023-12-13T12:00:46Z)
• Retrieval-augmented Multi-modal Chain-of-Thoughts Reasoning for Large Language Models [56.256069117502385]
  Chain of Thought (CoT) approaches can be used to enhance the capability of Large Language Models (LLMs) on complex reasoning tasks. However, the selection of optimal CoT demonstration examples in multi-modal reasoning remains less explored. We introduce a novel approach that addresses this challenge by using retrieval mechanisms to automatically select demonstration examples.
  arXiv  Detail & Related papers  (2023-12-04T08:07:21Z)
• Enhancing Deep Learning Models through Tensorization: A Comprehensive Survey and Framework [0.0]
  This paper explores the steps involved in multidimensional data sources, various multiway analysis methods employed, and the benefits of these approaches. A small example of Blind Source Separation (BSS) is presented comparing 2-dimensional algorithms and a multiway algorithm in Python. Results indicate that multiway analysis is more expressive.
  arXiv  Detail & Related papers  (2023-09-05T17:56:22Z)
• Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis [89.04041100520881]
  This research proposes to retrieve textual and visual evidence based on the object, sentence, and whole image. We develop a novel approach to synthesize the object-level, image-level, and sentence-level information for better reasoning between the same and different modalities.
  arXiv  Detail & Related papers  (2023-05-25T15:26:13Z)

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.