A survey of multimodal sentiment analysis
A survey of multimodal sentiment analysis
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DOI：
10.1016/j.imavis.2017.08.003
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发表时间：
2017-09-01
影响因子：
4.7
通讯作者：
Pantic, Maja
中科院分区：
文献类型：
--
作者：
Soleymani, Mohammad;Garcia, David;Pantic, Maja
情绪分析旨在自动揭示我们对实体的潜在态度。将这些情绪聚集在一个群体中就代表了民意测验，并有许多应用。当前基于文本的情感分析依赖于从大型文本语料库中学习情感的词典和机器学习模型的构建。文本情感分析目前广泛用于客户满意度评估和品牌感知分析等。随着社交媒体的普及，多模态情感分析将带来新的机遇，补充数据流的到来将改善和超越基于文本的情感分析。由于情感可以通过它留下的情感痕迹（如面部和声音显示）来检测，因此多模态情感分析提供了分析面部和声音表达以及文字或文本内容的有希望的途径。这些方法利用情绪识别和上下文推理来确定个人情绪的潜在极性和范围。在这次调查中，我们定义了情感和多模态情感分析的问题，并回顾了多模态情感分析在不同领域的最新发展，包括口语评论，图像，视频博客，人机和人机交互。这一新兴领域的挑战和机遇也进行了讨论，导致我们的论文，多模态情感分析具有显着的未开发的潜力。(C)2017爱思唯尔B.V.保留所有权利。
Sentiment analysis aims to automatically uncover the underlying attitude that we hold towards an entity. The aggregation of these sentiments over a population represents opinion polling and has numerous applications. Current text-based sentiment analysis relies on the construction of dictionaries and machine learning models that learn sentiment from large text corpora. Sentiment analysis from text is currently widely used for customer satisfaction assessment and brand perception analysis, among others. With the proliferation of social media, multimodal sentiment analysis is set to bring new opportunities with the arrival of complementary data streams for improving and going beyond text-based sentiment analysis. Since sentiment can be detected through affective traces it leaves, such as facial and vocal displays, multimodal sentiment analysis offers promising avenues for analyzing facial and vocal expressions in addition to the transcript or textual content. These approaches leverage emotion recognition and context inference to determine the underlying polarity and scope of an individual's sentiment. In this survey, we define sentiment and the problem of multimodal sentiment analysis and review recent developments in multimodal sentiment analysis in different domains, including spoken reviews, images, video blogs, human machine and human human interactions. Challenges and opportunities of this emerging field are also discussed, leading to our thesis that multimodal sentiment analysis holds a significant untapped potential. (C) 2017 Elsevier B.V. All rights reserved.