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Complura: Exploring and Leveraging a Large-scale Multilingual Visual Sentiment Ontology

Authors:

Shih-Fu ChangAuthors Info & Claims

ICMR '16: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval

Pages 417 - 420

https://doi.org/10.1145/2911996.2912030

Published: 06 June 2016 Publication History

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Abstract

What would someone from another culture think of this photograph I just took? Would they think my picture of this "wilted flower" was also sentimentally positive or would they perceive it negatively instead? Or what if I wanted to find other photographs that are semantically related to my image as well as sentimentally sensitive, but from other cultures? In fact, this cultural and sentimental relevancy are features that we would expect of any recommender system and query expansion engine, respectively. Motivated by this, we present an online demonstration of a system called Complura. Our system implements three major functions: an interactive multilingual ontology browser, a cross-lingual image-based sentiment analyzer, and a culturally-coherent, sentiment-aware image query expansion engine. We ground our system on a multilingual visual sentiment ontology, containing over 10k sentiment-polarized visual concepts over 12 languages and over 7.3M images.

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Cited By

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Das RSingh T(2023)Multimodal Sentiment Analysis: A Survey of Methods, Trends, and ChallengesACM Computing Surveys10.1145/358607555:13s(1-38)Online publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1145/3586075
Park EStorey V(2023)Emotion Ontology Studies: A Framework for Expressing Feelings Digitally and its Application to Sentiment AnalysisACM Computing Surveys10.1145/355571955:9(1-38)Online publication date: 16-Jan-2023
https://dl.acm.org/doi/10.1145/3555719
Yoon SKim H(2022)What content and context factors lead to selection of a video clip? The heuristic route perspectiveElectronic Commerce Research10.1007/s10660-019-09355-619:3(603-627)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1007/s10660-019-09355-6
Show More Cited By

Index Terms

Complura: Exploring and Leveraging a Large-scale Multilingual Visual Sentiment Ontology
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Information visualization
    2. Visualization systems and tools
      1. Visualization toolkits
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
      2. Sentiment analysis
  2. Information systems applications
    1. Multimedia information systems
      1. Multimedia databases

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Published In

ICMR '16: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval

June 2016

452 pages

ISBN:9781450343596

DOI:10.1145/2911996

General Chairs:
John R. Kender
Columbia University, USA
,
John R. Smith
IBM Research, USA
,
Program Chairs:
Jiebo Luo
University of Rochester, USA
,
Susanne Boll
University of Oldenburg, Germany
,
Winston Hsu
National Taiwan University, Taiwan

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 June 2016

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SIGMM

ICMR'16: International Conference on Multimedia Retrieval

June 6 - 9, 2016

New York, New York, USA

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ICMR '16 Paper Acceptance Rate 20 of 120 submissions, 17%;

Overall Acceptance Rate 254 of 830 submissions, 31%

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Das RSingh T(2023)Multimodal Sentiment Analysis: A Survey of Methods, Trends, and ChallengesACM Computing Surveys10.1145/358607555:13s(1-38)Online publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1145/3586075
Park EStorey V(2023)Emotion Ontology Studies: A Framework for Expressing Feelings Digitally and its Application to Sentiment AnalysisACM Computing Surveys10.1145/355571955:9(1-38)Online publication date: 16-Jan-2023
https://dl.acm.org/doi/10.1145/3555719
Yoon SKim H(2022)What content and context factors lead to selection of a video clip? The heuristic route perspectiveElectronic Commerce Research10.1007/s10660-019-09355-619:3(603-627)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1007/s10660-019-09355-6
Li ZFan YJiang BLei TLiu W(2019)A survey on sentiment analysis and opinion mining for social multimediaMultimedia Tools and Applications10.1007/s11042-018-6445-z78:6(6939-6967)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s11042-018-6445-z
Hamano SOgawa THaseyama M(2018)A Language-Independent Ontology Construction Method Using Tagged Images in FolksonomyIEEE Access10.1109/ACCESS.2017.27862186(2930-2942)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2017.2786218
Hussain ZZhang MZhang XYe KThomas CAgha ZOng NKovashka A(2017)Automatic Understanding of Image and Video Advertisements2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR.2017.123(1100-1110)Online publication date: Jul-2017
https://doi.org/10.1109/CVPR.2017.123
Pappas NRedi MTopkara MLiu HJou BChen TChang S(2017)Multilingual visual sentiment concept clustering and analysisInternational Journal of Multimedia Information Retrieval10.1007/s13735-017-0120-46:1(51-70)Online publication date: 20-Feb-2017
https://doi.org/10.1007/s13735-017-0120-4
Pappas NRedi MTopkara MJou BLiu HChen TChang SKender JSmith JLuo JBoll SHsu W(2016)Multilingual Visual Sentiment Concept MatchingProceedings of the 2016 ACM on International Conference on Multimedia Retrieval10.1145/2911996.2912016(151-158)Online publication date: 6-Jun-2016
https://dl.acm.org/doi/10.1145/2911996.2912016

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Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology

Supervised sentiment analysis in multilingual environments

Creating sentiment dictionaries via triangulation

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