<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="arpan-asif-2023-qemma">
    <titleInfo>
        <title>QeMMA: Quantum-Enhanced Multi-Modal Sentiment Analysis</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Phukan</namePart>
        <namePart type="family">Arpan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ekbal</namePart>
        <namePart type="family">Asif</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 20th International Conference on Natural Language Processing (ICON)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">D</namePart>
            <namePart type="given">Pawar</namePart>
            <namePart type="family">Jyoti</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lalitha</namePart>
            <namePart type="given">Devi</namePart>
            <namePart type="family">Sobha</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>NLP Association of India (NLPAI)</publisher>
            <place>
                <placeTerm type="text">Goa University, Goa, India</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Multi-modal data analysis presents formidable challenges, as developing effective methods to capture correlations among different modalities remains an ongoing pursuit. In this study, we address multi-modal sentiment analysis through a novel quantum perspective. We propose that quantum principles, such as superposition, entanglement, and interference, offer a more comprehensive framework for capturing not only the cross-modal interactions between text, acoustics, and visuals but also the intricate relations within each modality. To empirically evaluate our approach, we employ the CMUMOSEI dataset as our testbed and utilize Qiskit by IBM to run our experiments on a quantum computer. Our proposed Quantum-Enhanced Multi-Modal Analysis Framework (QeMMA) showcases its significant potential by surpassing the baseline by 3.52% and 10.14% in terms of accuracy and F1 score, respectively, highlighting the promise of quantum-inspired methodologies.</abstract>
    <identifier type="citekey">arpan-asif-2023-qemma</identifier>
    <location>
        <url>https://aclanthology.org/2023.icon-1.84</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>815</start>
            <end>821</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T QeMMA: Quantum-Enhanced Multi-Modal Sentiment Analysis
%A Arpan, Phukan
%A Asif, Ekbal
%Y Jyoti, D. Pawar
%Y Sobha, Lalitha Devi
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F arpan-asif-2023-qemma
%X Multi-modal data analysis presents formidable challenges, as developing effective methods to capture correlations among different modalities remains an ongoing pursuit. In this study, we address multi-modal sentiment analysis through a novel quantum perspective. We propose that quantum principles, such as superposition, entanglement, and interference, offer a more comprehensive framework for capturing not only the cross-modal interactions between text, acoustics, and visuals but also the intricate relations within each modality. To empirically evaluate our approach, we employ the CMUMOSEI dataset as our testbed and utilize Qiskit by IBM to run our experiments on a quantum computer. Our proposed Quantum-Enhanced Multi-Modal Analysis Framework (QeMMA) showcases its significant potential by surpassing the baseline by 3.52% and 10.14% in terms of accuracy and F1 score, respectively, highlighting the promise of quantum-inspired methodologies.
%U https://aclanthology.org/2023.icon-1.84
%P 815-821

Markdown (Informal)

[QeMMA: Quantum-Enhanced Multi-Modal Sentiment Analysis](https://aclanthology.org/2023.icon-1.84) (Arpan & Asif, ICON 2023)

• QeMMA: Quantum-Enhanced Multi-Modal Sentiment Analysis (Arpan & Asif, ICON 2023)

ACL

• Phukan Arpan and Ekbal Asif. 2023. QeMMA: Quantum-Enhanced Multi-Modal Sentiment Analysis. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 815–821, Goa University, Goa, India. NLP Association of India (NLPAI).