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Visual Sentiment Analysis Based on on Objective Text Description of Images

机译:基于图像客观文本描述的视觉情感分析

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Visual Sentiment Analysis aims to estimate the polarity of the sentiment evoked by images in terms of positive or negative sentiment. To this aim, most of the state of the art works exploit the text associated to a social post provided by the user. However, such textual data is typically noisy due to the subjectivity of the user which usually includes text useful to maximize the diffusion of the social post. In this paper we extract and employ an Objective Text description of images automatically extracted from the visual content rather than the classic Subjective Text provided by the users. The proposed method defines a multimodal embedding space based on the contribute of both visual and textual features. The sentiment polarity is then inferred by a supervised Support Vector Machine trained on the representations of the obtained embedding space. Experiments performed on a representative dataset of 47235 labelled samples demonstrate that the exploitation of the proposed Objective Text helps to outperform state-of-the-art for sentiment polarity estimation.
机译:视觉情感分析的目的是根据积极情绪或消极情绪来估计图像诱发的情感极性。为此,大多数现有技术利用与用户提供的社交帖子相关的文本。然而,由于用户的主观性,这样的文本数据通常是嘈杂的,用户的主观性通常包括用于最大化社交帖子的传播的文本。在本文中,我们提取并采用从视觉内容中自动提取的图像的客观文本描述,而不是用户提供的经典主观文本。所提出的方法基于视觉和文本特征的贡献定义了一种多峰嵌入空间。然后,通过对获得的嵌入空间的表示进行训练的监督支持向量机,可以推断出情感的极性。在47235个标记样本的代表性数据集上进行的实验表明,所提出的“目标文本”的利用有助于超越情绪极性估计的最新技术。

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