Multimodal Sentiment Analysis: A Survey

• URL: http://arxiv.org/abs/2305.07611v3
• Date: Mon, 3 Jul 2023 19:05:30 GMT
• Title: Multimodal Sentiment Analysis: A Survey
• Authors: Songning Lai, Xifeng Hu, Haoxuan Xu, Zhaoxia Ren and Zhi Liu
• Abstract summary: This review provides an overview of the definition, background, and development of multimodal sentiment analysis. It also covers recent datasets and advanced models, emphasizing the challenges and future prospects of this technology.
• Score: 5.910558593808665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal sentiment analysis has become an important research area in the field of artificial intelligence. With the latest advances in deep learning, this technology has reached new heights. It has great potential for both application and research, making it a popular research topic. This review provides an overview of the definition, background, and development of multimodal sentiment analysis. It also covers recent datasets and advanced models, emphasizing the challenges and future prospects of this technology. Finally, it looks ahead to future research directions. It should be noted that this review provides constructive suggestions for promising research directions and building better performing multimodal sentiment analysis models, which can help researchers in this field.

Related papers

• 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)
• From Linguistic Giants to Sensory Maestros: A Survey on Cross-Modal Reasoning with Large Language Models [56.9134620424985]
  Cross-modal reasoning (CMR) is increasingly recognized as a crucial capability in the progression toward more sophisticated artificial intelligence systems. The recent trend of deploying Large Language Models (LLMs) to tackle CMR tasks has marked a new mainstream of approaches for enhancing their effectiveness. This survey offers a nuanced exposition of current methodologies applied in CMR using LLMs, classifying these into a detailed three-tiered taxonomy.
  arXiv  Detail & Related papers  (2024-09-19T02:51:54Z)
• Neural Radiance Field-based Visual Rendering: A Comprehensive Review [0.6047429555885261]
  In recent years, Neural Radiance Fields (NeRF) has made remarkable progress in the field of computer vision and graphics. NeRF has caused a continuous research boom in the academic community. This review provides an in-depth analysis of the research literature on NeRF within the past two years.
  arXiv  Detail & Related papers  (2024-03-31T15:18:38Z)
• Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models [52.24001776263608]
  This comprehensive survey delves into the recent strides in HS moderation. We highlight the burgeoning role of large language models (LLMs) and large multimodal models (LMMs) We identify existing gaps in research, particularly in the context of underrepresented languages and cultures.
  arXiv  Detail & Related papers  (2024-01-30T03:51:44Z)
• A Survey of Reasoning with Foundation Models [235.7288855108172]
  Reasoning plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation. We introduce seminal foundation models proposed or adaptable for reasoning. We then delve into the potential future directions behind the emergence of reasoning abilities within foundation models.
  arXiv  Detail & Related papers  (2023-12-17T15:16:13Z)
• A Survey on Interpretable Cross-modal Reasoning [64.37362731950843]
  Cross-modal reasoning (CMR) has emerged as a pivotal area with applications spanning from multimedia analysis to healthcare diagnostics. This survey delves into the realm of interpretable cross-modal reasoning (I-CMR) This survey presents a comprehensive overview of the typical methods with a three-level taxonomy for I-CMR.
  arXiv  Detail & Related papers  (2023-09-05T05:06:48Z)
• Enhancing Artificial intelligence Policies with Fusion and Forecasting: Insights from Indian Patents Using Network Analysis [0.0]
  This paper presents a study of the interconnectivity and interdependence of various Artificial intelligence (AI) technologies. By analyzing the technologies through different time windows and quantifying their importance, we have revealed important insights into the crucial components shaping the AI landscape.
  arXiv  Detail & Related papers  (2023-04-20T18:37:11Z)
• Researching Alignment Research: Unsupervised Analysis [14.699455652461726]
  AI alignment research is dedicated to ensuring that artificial intelligence (AI) benefits humans. In this project, we collected and analyzed existing AI alignment research. We found that the field is growing quickly, with several subfields emerging in parallel.
  arXiv  Detail & Related papers  (2022-06-06T18:24:17Z)
• Characterising Research Areas in the field of AI [68.8204255655161]
  We identified the main conceptual themes by performing clustering analysis on the co-occurrence network of topics. The results highlight the growing academic interest in research themes like deep learning, machine learning, and internet of things.
  arXiv  Detail & Related papers  (2022-05-26T16:30:30Z)
• 3D Object Detection from Images for Autonomous Driving: A Survey [68.33502122185813]
  3D object detection from images is one of the fundamental and challenging problems in autonomous driving. More than 200 works have studied this problem from 2015 to 2021, encompassing a broad spectrum of theories, algorithms, and applications. We provide the first comprehensive survey of this novel and continuously growing research field, summarizing the most commonly used pipelines for image-based 3D detection.
  arXiv  Detail & Related papers  (2022-02-07T07:12:24Z)
• Deep Learning for Road Traffic Forecasting: Does it Make a Difference? [6.220008946076208]
  This paper focuses on critically analyzing the state of the art in what refers to the use of Deep Learning for this particular ITS research area. A posterior critical analysis is held to formulate questions and trigger a necessary debate about the issues of Deep Learning for traffic forecasting.
  arXiv  Detail & Related papers  (2020-12-02T15:56:11Z)

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.