Image sentiment prediction based on textual descriptions with adjective noun pairs

Li, Zuhe; Fan, Yangyu; Liu, Weihua; Wang, Fengqin

doi:10.1007/s11042-016-4310-5

Cited by 36 publications

(11 citation statements)

References 18 publications

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“…Li et al [40] further compute the weighted sum of the textual sentiment values of ANPs describing the image and take the textual sentiment into account. Yuan et al [41] propose the Sentribute, an image-sentiment analysis algorithm based on 102 mid-level attributes, which are easier to interpret and ready to use for high-level understanding.…”

Section: A Affective Image Predictionmentioning

confidence: 99%

Visual Sentiment Prediction Based on Automatic Discovery of Affective Regions

Yang

She

Sun

et al. 2018

IEEE Trans. Multimedia

151

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Abstract-Automatic assessment of sentiment from visual content has gained considerable attention with the increasing tendency of expressing opinions via images and videos online. This paper investigates the problem of visual sentiment analysis, which involves a high-level abstraction in the recognition process. While most of the current methods focus on improving holistic representations, we aim to utilize the local information, which is inspired by the observation that both the whole image and local regions convey significant sentiment information. We propose a framework to leverage affective regions, where we first use an off-the-shelf objectness tool to generate the candidates, and employ a candidate selection method to remove redundant and noisy proposals. Then a convolutional neural network (CNN) is connected with each candidate to compute the sentiment scores, and the affective regions are automatically discovered, taking the objectness score as well as the sentiment score into consideration. Finally, the CNN outputs from local regions are aggregated with the whole images to produce the final predictions. Our framework only requires image-level labels, thereby significantly reducing the annotation burden otherwise required for training. This is especially important for sentiment analysis as sentiment can be abstract, and labeling affective regions is too subjective and labor-consuming. Extensive experiments show that the proposed algorithm outperforms the state-of-the-art approaches on eight popular benchmark datasets.

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Section: A Affective Image Predictionmentioning

confidence: 99%

Visual Sentiment Prediction Based on Automatic Discovery of Affective Regions

Yang

She

Sun

et al. 2018

IEEE Trans. Multimedia

151

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show abstract

“…Borth et al [ 16 ] proposed a form of multiple adjective noun pairs to describe image content, called ANPs; Chen et al [ 17 ] further proposed a classification CNN called DeepSentiBank based on visual sentiment concepts. Li et al [ 18 ] further combined the text information in ANPs and calculated the emotional information of the text value in ANPs in the form of weighted sum. Yuan et al [ 19 ] proposed an algorithm called Sentribute with 102 mid-level attributes that is readily comprehensible and can be used for higher-level sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

Multi-Level Context Pyramid Network for Visual Sentiment Analysis

Qing

et al. 2021

Sensors

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Sharing our feelings through content with images and short videos is one main way of expression on social networks. Visual content can affect people’s emotions, which makes the task of analyzing the sentimental information of visual content more and more concerned. Most of the current methods focus on how to improve the local emotional representations to get better performance of sentiment analysis and ignore the problem of how to perceive objects of different scales and different emotional intensity in complex scenes. In this paper, based on the alterable scale and multi-level local regional emotional affinity analysis under the global perspective, we propose a multi-level context pyramid network (MCPNet) for visual sentiment analysis by combining local and global representations to improve the classification performance. Firstly, Resnet101 is employed as backbone to obtain multi-level emotional representation representing different degrees of semantic information and detailed information. Next, the multi-scale adaptive context modules (MACM) are proposed to learn the sentiment correlation degree of different regions for different scale in the image, and to extract the multi-scale context features for each level deep representation. Finally, different levels of context features are combined to obtain the multi-cue sentimental feature for image sentiment classification. Extensive experimental results on seven commonly used visual sentiment datasets illustrate that our method outperforms the state-of-the-art methods, especially the accuracy on the FI dataset exceeds 90%.

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“…The system proposed in [54] represents the sentiment of an image by extracting a set of ANPs describing the image. Then, the weighted sum of the extracted textual sentiment values is computed, by using the related ANP responses as weights.…”

Section: State‐of‐the‐artmentioning

confidence: 99%