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DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks

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arxiv 1410.8586 v1 pith:K27CNPCF submitted 2014-10-30 cs.CV cs.LGcs.MMcs.NE

Tao Chen , Damian Borth , Trevor Darrell , Shih-Fu Chang This is my paper · ORCID
classification cs.CVcs.LGcs.MMcs.NE
keywords deepmodelsentimentclassificationcnnsconvolutionalimagesnetworks
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper introduces a visual sentiment concept classification method based on deep convolutional neural networks (CNNs). The visual sentiment concepts are adjective noun pairs (ANPs) automatically discovered from the tags of web photos, and can be utilized as effective statistical cues for detecting emotions depicted in the images. Nearly one million Flickr images tagged with these ANPs are downloaded to train the classifiers of the concepts. We adopt the popular model of deep convolutional neural networks which recently shows great performance improvement on classifying large-scale web-based image dataset such as ImageNet. Our deep CNNs model is trained based on Caffe, a newly developed deep learning framework. To deal with the biased training data which only contains images with strong sentiment and to prevent overfitting, we initialize the model with the model weights trained from ImageNet. Performance evaluation shows the newly trained deep CNNs model SentiBank 2.0 (or called DeepSentiBank) is significantly improved in both annotation accuracy and retrieval performance, compared to its predecessors which mainly use binary SVM classification models.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Discrete Prompt Tuning via Recursive Utilization of Black-box Multimodal Large Language Model for Personalized Visual Emotion Recognition

    cs.CL 2025-08 conditional novelty 5.0 of 10

    Recursive generation, evaluation, and refinement of discrete prompts tunes a black-box multimodal LLM to each user, raising personalized visual emotion recognition accuracy on Affection from 40.6% to 44.9%.

  2. Enhanced Multimodal Aspect-Based Sentiment Analysis by LLM-Generated Rationales

    cs.CL 2025-05 reject novelty 5.0 of 10

    Feeding LLM-generated text and image rationales into fine-tuned small models with a dual cross-attention module improves multimodal aspect-based sentiment analysis by 1-2 F1 points on Twitter2015 and Twitter2017.

  3. Automatic Modeling of Social Concepts Evoked by Art Images as Multimodal Frames

    cs.CV 2021-10 unverdicted novelty 5.0 of 10

    The authors introduce a conceptual model and novel ontology to represent social concepts evoked by art images as multimodal frames, demonstrated via a proof-of-concept experiment on the Tate Gallery collection.

  4. Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding

    cs.CV 2025-08 unverdicted novelty 3.0 of 10

    A literature survey on abstract concept recognition in videos that catalogs prior tasks and datasets while advocating for foundation models and reuse of decades of community experience.

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