Enhancing Consumer Insights Through Multimodal Artificial Intelligence and Affective Computing
The growing interest in learning more about consumer behaviors through analytical techniques requires the integration of innovative approaches that relate their needs to strategic marketing procedures. Multimodality and Affective Computing combined a series of robust optimizations for this challenge...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11072457/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839629654154018816 |
|---|---|
| author | Ines Cesar Ivo Pereira Fatima Rodrigues Vera L. Migueis Susana Nicola Ana Madureira Jose Luis Reis Jose Paulo Marques Dos Santos Duarte Coelho Daniel Alves De Oliveira |
| author_facet | Ines Cesar Ivo Pereira Fatima Rodrigues Vera L. Migueis Susana Nicola Ana Madureira Jose Luis Reis Jose Paulo Marques Dos Santos Duarte Coelho Daniel Alves De Oliveira |
| author_sort | Ines Cesar |
| collection | DOAJ |
| description | The growing interest in learning more about consumer behaviors through analytical techniques requires the integration of innovative approaches that relate their needs to strategic marketing procedures. Multimodality and Affective Computing combined a series of robust optimizations for this challenge, implying the complexity of each application. However, the entanglement of different modalities demands new and tailored refinements to enhance adaptability and accuracy in the field. This paper outlines the implementation of a Multimodal Artificial Intelligence methodology with Affective Computing to enhance consumer insights and marketing strategies. The application combines different data modalities, such as textual, visual, and audio inputs, to tackle complex issues in dealing with consumer sentiment. The proposed approach uses advanced preprocessing techniques, including word embeddings, neural networks, and recurrent models, to extract information from diverse modalities. Fusion strategies, such as attention-based and late fusion procedures, are utilized to combine knowledge, facilitating robust sentiment detection. The implementation includes the analysis of real-time customer feedback on social media and product assessments, demonstrating improvements in predicting engagement and shaping consumer behavior. The results underscore the practical viability of the suggested method, promoting progress in multimodal sentiment analysis to extract actionable consumer insights in marketing. |
| format | Article |
| id | doaj-art-352b80df8c6e41b6a4faa5563ff8f54e |
| institution | Matheson Library |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-352b80df8c6e41b6a4faa5563ff8f54e2025-07-14T23:00:51ZengIEEEIEEE Access2169-35362025-01-011311870611872310.1109/ACCESS.2025.358671311072457Enhancing Consumer Insights Through Multimodal Artificial Intelligence and Affective ComputingInes Cesar0https://orcid.org/0009-0005-3915-7421Ivo Pereira1https://orcid.org/0000-0001-5440-3225Fatima Rodrigues2Vera L. Migueis3https://orcid.org/0000-0001-7831-9140Susana Nicola4https://orcid.org/0000-0003-1494-7521Ana Madureira5https://orcid.org/0000-0002-0264-4710Jose Luis Reis6https://orcid.org/0000-0002-0987-0980Jose Paulo Marques Dos Santos7https://orcid.org/0000-0002-5567-944XDuarte Coelho8https://orcid.org/0000-0003-2665-8057Daniel Alves De Oliveira9https://orcid.org/0000-0002-8225-5984Interdisciplinary Studies Research Center (ISRC), Instituto Superior de Engenharia do Porto, Porto, PortugalInterdisciplinary Studies Research Center (ISRC), Instituto Superior de Engenharia do Porto, Porto, PortugalInterdisciplinary Studies Research Center (ISRC), Instituto Superior de Engenharia do Porto, Porto, PortugalInstituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência (INESC TEC), Faculdade de Engenharia da Universidade do Porto, Porto, PortugalInterdisciplinary Studies Research Center (ISRC), Instituto Superior de Engenharia do Porto, Porto, PortugalInterdisciplinary Studies Research Center (ISRC), Instituto Superior de Engenharia do Porto, Porto, PortugalDepartment of Business Administration, Universidade da Maia, Maia, PortugalDepartment of Business Administration, Universidade da Maia, Maia, PortugalInterdisciplinary Studies Research Center (ISRC), Instituto Superior de Engenharia do Porto, Porto, PortugalE-goi, Matosinhos, PortugalThe growing interest in learning more about consumer behaviors through analytical techniques requires the integration of innovative approaches that relate their needs to strategic marketing procedures. Multimodality and Affective Computing combined a series of robust optimizations for this challenge, implying the complexity of each application. However, the entanglement of different modalities demands new and tailored refinements to enhance adaptability and accuracy in the field. This paper outlines the implementation of a Multimodal Artificial Intelligence methodology with Affective Computing to enhance consumer insights and marketing strategies. The application combines different data modalities, such as textual, visual, and audio inputs, to tackle complex issues in dealing with consumer sentiment. The proposed approach uses advanced preprocessing techniques, including word embeddings, neural networks, and recurrent models, to extract information from diverse modalities. Fusion strategies, such as attention-based and late fusion procedures, are utilized to combine knowledge, facilitating robust sentiment detection. The implementation includes the analysis of real-time customer feedback on social media and product assessments, demonstrating improvements in predicting engagement and shaping consumer behavior. The results underscore the practical viability of the suggested method, promoting progress in multimodal sentiment analysis to extract actionable consumer insights in marketing.https://ieeexplore.ieee.org/document/11072457/Affective computingartificial intelligencecustomer behaviormarketingmultimodal |
| spellingShingle | Ines Cesar Ivo Pereira Fatima Rodrigues Vera L. Migueis Susana Nicola Ana Madureira Jose Luis Reis Jose Paulo Marques Dos Santos Duarte Coelho Daniel Alves De Oliveira Enhancing Consumer Insights Through Multimodal Artificial Intelligence and Affective Computing IEEE Access Affective computing artificial intelligence customer behavior marketing multimodal |
| title | Enhancing Consumer Insights Through Multimodal Artificial Intelligence and Affective Computing |
| title_full | Enhancing Consumer Insights Through Multimodal Artificial Intelligence and Affective Computing |
| title_fullStr | Enhancing Consumer Insights Through Multimodal Artificial Intelligence and Affective Computing |
| title_full_unstemmed | Enhancing Consumer Insights Through Multimodal Artificial Intelligence and Affective Computing |
| title_short | Enhancing Consumer Insights Through Multimodal Artificial Intelligence and Affective Computing |
| title_sort | enhancing consumer insights through multimodal artificial intelligence and affective computing |
| topic | Affective computing artificial intelligence customer behavior marketing multimodal |
| url | https://ieeexplore.ieee.org/document/11072457/ |
| work_keys_str_mv | AT inescesar enhancingconsumerinsightsthroughmultimodalartificialintelligenceandaffectivecomputing AT ivopereira enhancingconsumerinsightsthroughmultimodalartificialintelligenceandaffectivecomputing AT fatimarodrigues enhancingconsumerinsightsthroughmultimodalartificialintelligenceandaffectivecomputing AT veralmigueis enhancingconsumerinsightsthroughmultimodalartificialintelligenceandaffectivecomputing AT susananicola enhancingconsumerinsightsthroughmultimodalartificialintelligenceandaffectivecomputing AT anamadureira enhancingconsumerinsightsthroughmultimodalartificialintelligenceandaffectivecomputing AT joseluisreis enhancingconsumerinsightsthroughmultimodalartificialintelligenceandaffectivecomputing AT josepaulomarquesdossantos enhancingconsumerinsightsthroughmultimodalartificialintelligenceandaffectivecomputing AT duartecoelho enhancingconsumerinsightsthroughmultimodalartificialintelligenceandaffectivecomputing AT danielalvesdeoliveira enhancingconsumerinsightsthroughmultimodalartificialintelligenceandaffectivecomputing |