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Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis

arXiv (Cornell University)(2021)10.1145/3462244.3479919Source: DataRank Database

Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis is a research paper published in arXiv (Cornell University) (2021). On theSindex it has a DataRank of 0. It has been cited 8 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access8 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (1 grants)Impact (FWCI 1.03)OA: greenTopics (16)IDs (PubMed, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (6)

Hui Chen ORCID,Alexander Gelbukh ORCID,Amir Zadeh,Louis–Philippe Morency ORCID,Soujanya Poria ORCID

Live enrichment

PubMed:19910364 OpenAlex:https://openalex.org/W3183580551

MOE Tier 1

Grant: T1SRIS19149

FWCI

1.03

Citation Percentile

0.8%

Citation Trend

2023

2024

2025

greenLicense: cc-by-sa

repositoryhttps://arxiv.org/pdf/2107.13669

publisherhttps://dl.acm.org/doi/10.1145/3462244.3479919

publisherhttps://dl.acm.org/doi/pdf/10.1145/3462244.3479919

repositoryhttp://arxiv.org/abs/2107.13669

Fields of Study

Sentiment Analysis and Opinion MiningEmotion and Mood RecognitionAdvanced Text Analysis Techniques

Keywords

ModalitiesComputer scienceLeverage (statistics)Modality (human–computer interaction)Artificial intelligencePairwise comparisonRelevance (law)Sentiment analysisSensor fusionFusionData miningMachine learningPattern recognition (psychology)

Sustainable Development Goals

SDG 0: Industry, innovation and infrastructure