NDLI: Large-scale visual sentiment ontology and detectors using adjective noun pairs

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Large-scale visual sentiment ontology and detectors using adjective noun pairs

Content Provider	ACM Digital Library
Author	Borth, Damian Ji, Rongrong Chen, Tao Chang, Shih-Fu Breuel, Thomas
Abstract	We address the challenge of sentiment analysis from visual content. In contrast to existing methods which infer sentiment or emotion directly from visual low-level features, we propose a novel approach based on understanding of the visual concepts that are strongly related to sentiments. Our key contribution is two-fold: first, we present a method built upon psychological theories and web mining to automatically construct a large-scale Visual Sentiment Ontology (VSO) consisting of more than 3,000 Adjective Noun Pairs (ANP). Second, we propose SentiBank, a novel visual concept detector library that can be used to detect the presence of 1,200 ANPs in an image. The VSO and SentiBank are distinct from existing work and will open a gate towards various applications enabled by automatic sentiment analysis. Experiments on detecting sentiment of image tweets demonstrate significant improvement in detection accuracy when comparing the proposed SentiBank based predictors with the text-based approaches. The effort also leads to a large publicly available resource consisting of a visual sentiment ontology, a large detector library, and the training/testing benchmark for visual sentiment analysis.
Starting Page	223
Ending Page	232
Page Count	10
File Format	PDF
ISBN	9781450324045
DOI	10.1145/2502081.2502282
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Sentiment prediction Ontology Social multimedia Concept detection
Content Type	Text
Resource Type	Article

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