Affective Embedding Framework with Semantic Representations from Tweets for Zero-Shot Visual Sentiment Prediction
Yingrui Ye, Yuya Moroto, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama · 2022
This paper presents a zero-shot visual sentiment prediction method using semantic representation features of texts from tweets as the non-visual auxiliary data. Previous studies show that visual sentiment prediction methods can only predict the sentiment labels that are the same as the labels of the sentiment theory used in the training dataset, which means that they cannot predict the new sentiment label used in different sentiment theories. To solve the problem of predicting new labels, zero-shot learning has been proposed. The previous zero-shot visual sentiment prediction method uses Word2vec features and the adjective-noun pair features to obtain the semantical relationship between images and sentiment words to predict unseen sentiments. However, many adjective-noun pairs are not related to sentiments, which makes it difficult to compensate for an affective gap between low-level visual features and high-level sentiment semantics. Thus, to better compensate for the affective gap, it is considered to introduce the new non-visual auxiliary data. As people tend to share their feelings with both images and texts on social networking services, the texts from tweets are effective as the side information of the images in visual sentiment prediction. Thus, we introduce the semantic representations from tweets as the new non-visual auxiliary data to construct an affective embedding space, which makes a more effective zero-shot visual sentiment prediction model. Moreover, we propose a cross-dataset zero-shot task for visual sentiment prediction, which is more consistent with the real situation that the testing and training images may be in different domains. The contributions in this paper are to combine several semantic representation features for zero-shot visual sentiment prediction and the proposal of the cross-dataset zero-shot task for visual sentiment prediction. The experiments on several open datasets show the effectiveness of the proposed method.