Profile-level positivity assessment using multimodal feature aggregation from Social Media Content

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dc.contributor.authorGorraiz Bengoechea, Marta
dc.contributor.authorGonzález Santocildes, Asier
dc.contributor.authorPastor López, Iker
dc.contributor.authorEstévez Gutiérrez, Ana
dc.contributor.authorAonso Diego, Gema
dc.date.accessioned2026-04-13T15:13:17Z
dc.date.available2026-04-13T15:13:17Z
dc.date.issued2026-02-27
dc.date.updated2026-04-13T15:13:17Z
dc.description.abstractAccurately evaluating positivity in social media profiles has important implications for advertising, mental health monitoring, and responsible AI applications. However, the multimodal nature of Instagram, where images, captions, hashtags, and emojis co-occur, challenges conventional unimodal sentiment analysis tools. We present a multimodal system that integrates computer vision and natural language processing to assess Instagram profiles. The architecture comprises two blocks: (i) feature extraction via facial-emotion analysis, object detection, chromatic cues, and linguistic processing; and (ii) profile-level inference, which fuses aggregated features to produce an overall positivity score together with semantically meaningful profile tags. Experiments on public Instagram profiles (images, captions, and comments), including an expert-annotated subset of comments, compare unimodal baselines with transformer-based embeddings and OpenAI large language models. The multimodal pipeline improves over unimodal variants, and the OpenAI approach achieves higher concordance with expert judgements than RoBERTa/Bi-LSTM baselines. Beyond technical contributions, we discuss privacy, transparency, and misuse risks associated with affective computing on social media. The proposed framework contributes to the advancement of multimodal sentiment analysis in social media and highlights its applicability to areas such as marketing analytics, well-being monitoring, and ethically aligned AI systems.en
dc.description.sponsorshipFunding for this study was provided by the Directorate General for the Regulation of Gambling (ref.: SUBV24/00009)en
dc.identifier.citationGorraiz-Bengoechea, M., Gonzalez-Santocildes, A., Pastor, I., Estévez, A., & Aonso-Diego, G. (2026). Profile-level positivity assessment using multimodal feature aggregation from Social Media Content. IEEE Access, 14, 37373-37383. https://doi.org/10.1109/ACCESS.2026.3668915
dc.identifier.doi10.1109/ACCESS.2026.3668915
dc.identifier.eissn2169-3536
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5628
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rights© 2026 The Authors
dc.subject.otherComputer vision
dc.subject.otherDeep learning
dc.subject.otherEthical AI
dc.subject.otherInstagram
dc.subject.otherMultimodal sentiment analysis
dc.subject.otherNatural language processing
dc.subject.otherSocial media analytics
dc.subject.otherTopic modeling
dc.titleProfile-level positivity assessment using multimodal feature aggregation from Social Media Contenten
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage37383
oaire.citation.startPage37373
oaire.citation.titleIEEE Access
oaire.citation.volume14
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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