NDLI: Sentribute: image sentiment analysis from a mid-level perspective

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Sentribute: image sentiment analysis from a mid-level perspective

Sentribute: image sentiment analysis from a mid-level perspective

Content Provider	ACM Digital Library
Author	Yuan, Jianbo Luo, Jiebo Mcdonough, Sean You, Quanzeng
Abstract	Visual content analysis has always been important yet challenging. Thanks to the popularity of social networks, images become an convenient carrier for information diffusion among online users. To understand the diffusion patterns and different aspects of the social images, we need to interpret the images first. Similar to textual content, images also carry different levels of sentiment to their viewers. However, different from text, where sentiment analysis can use easily accessible semantic and context information, how to extract and interpret the sentiment of an image remains quite challenging. In this paper, we propose an image sentiment prediction framework, which leverages the mid-level attributes of an image to predict its sentiment. This makes the sentiment classification results more interpretable than directly using the low-level features of an image. To obtain a better performance on images containing faces, we introduce eigenface-based facial expression detection as an additional mid-level attributes. An empirical study of the proposed framework shows improved performance in terms of prediction accuracy. More importantly, by inspecting the prediction results, we are able to discover interesting relationships between mid-level attribute and image sentiment.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781450323321
DOI	10.1145/2502069.2502079
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-08-11
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Analysis Visual content Image sentiment Mid-level attributes Application
Content Type	Text
Resource Type	Article

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