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Micro-opinion Sentiment Intensity Analysis and Summarization in Online Videos

Author:

Amir ZadehAuthors Info & Claims

ICMI '15: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction

Pages 587 - 591

https://doi.org/10.1145/2818346.2823317

Published: 09 November 2015 Publication History

Abstract

There has been substantial progress in the field of text based sentiment analysis but little effort has been made to incorporate other modalities. Previous work in sentiment analysis has shown that using multimodal data yields to more accurate models of sentiment. Efforts have been made towards expressing sentiment as a spectrum of intensity rather than just positive or negative. Such models are useful not only for detection of positivity or negativity, but also giving out a score of how positive or negative a statement is. Based on the state of the art studies in sentiment analysis, prediction in terms of sentiment score is still far from accurate, even in large datasets [27]. Another challenge in sentiment analysis is dealing with small segments or micro opinions as they carry less context than large segments thus making analysis of the sentiment harder. This paper presents a Ph.D. thesis shaped towards comprehensive studies in multimodal micro-opinion sentiment intensity analysis.

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Cited By

Singh UAbhishek KAzad H(2024)A Survey of Cutting-edge Multimodal Sentiment AnalysisACM Computing Surveys10.1145/365214956:9(1-38)Online publication date: 25-Apr-2024
https://dl.acm.org/doi/10.1145/3652149
Haouhat ABellaouar SNehar ACherroun H(2023)Towards Arabic Multimodal Dataset for Sentiment Analysis2023 Fourth International Conference on Intelligent Data Science Technologies and Applications (IDSTA)10.1109/IDSTA58916.2023.10317847(126-133)Online publication date: 24-Oct-2023
https://doi.org/10.1109/IDSTA58916.2023.10317847
Wang HLiu SZhao DChen YJi W(2023)Tri-Modalities Fusion for Multimodal Sentiment Analysis2023 7th Asian Conference on Artificial Intelligence Technology (ACAIT)10.1109/ACAIT60137.2023.10528533(1501-1506)Online publication date: 10-Nov-2023
https://doi.org/10.1109/ACAIT60137.2023.10528533
Show More Cited By

Index Terms

Micro-opinion Sentiment Intensity Analysis and Summarization in Online Videos
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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Published In

cover image ACM Conferences

ICMI '15: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction

November 2015

678 pages

ISBN:9781450339124

DOI:10.1145/2818346

General Chairs:
Zhengyou Zhang
Microsoft Research, USA
,
Phil Cohen
VoiceBox Technologies, USA
,
Program Chairs:
Dan Bohus
Microsoft Research, USA
,
Radu Horaud
INRIA Grenoble Rhone-Alpes, France
,
Helen Meng
Chinese University of Hong Kong, China

Copyright © 2015 ACM.

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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New York, NY, United States

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Published: 09 November 2015

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ICMI '15: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

November 9 - 13, 2015

Washington, Seattle, USA

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ICMI '15 Paper Acceptance Rate 52 of 127 submissions, 41%;

Overall Acceptance Rate 453 of 1,080 submissions, 42%

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Cited By

Singh UAbhishek KAzad H(2024)A Survey of Cutting-edge Multimodal Sentiment AnalysisACM Computing Surveys10.1145/365214956:9(1-38)Online publication date: 25-Apr-2024
https://dl.acm.org/doi/10.1145/3652149
Haouhat ABellaouar SNehar ACherroun H(2023)Towards Arabic Multimodal Dataset for Sentiment Analysis2023 Fourth International Conference on Intelligent Data Science Technologies and Applications (IDSTA)10.1109/IDSTA58916.2023.10317847(126-133)Online publication date: 24-Oct-2023
https://doi.org/10.1109/IDSTA58916.2023.10317847
Wang HLiu SZhao DChen YJi W(2023)Tri-Modalities Fusion for Multimodal Sentiment Analysis2023 7th Asian Conference on Artificial Intelligence Technology (ACAIT)10.1109/ACAIT60137.2023.10528533(1501-1506)Online publication date: 10-Nov-2023
https://doi.org/10.1109/ACAIT60137.2023.10528533
Narawade VDandekar A(2023)Sentiment Analysis Toward COVID-19 Vaccination Based on Twitter PostsSoft Computing and Signal Processing10.1007/978-981-19-8669-7_36(409-419)Online publication date: 27-Jun-2023
https://doi.org/10.1007/978-981-19-8669-7_36
DİKBIYIK EDEMİR ÖDOĞAN B(2022)Derin Öğrenme Yöntemleri İle Konuşmadan Duygu Tanıma Üzerine Bir Literatür AraştırmasıA Literature Review On Speech Emotion Recognition Using Deep Learning TechniquesGazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji10.29109/gujsc.111188410:4(765-791)Online publication date: 30-Dec-2022
https://doi.org/10.29109/gujsc.1111884
Huddar MSannakki SRajpurohit V(2020)Multi‐level feature optimization and multimodal contextual fusion for sentiment analysis and emotion classificationComputational Intelligence10.1111/coin.1227436:2(861-881)Online publication date: 21-Jan-2020
https://doi.org/10.1111/coin.12274
Thuseethan SJanarthan SRajasegarar SKumari PYearwood J(2020)Multimodal Deep Learning Framework for Sentiment Analysis from Text-Image Web Data2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00039(267-274)Online publication date: Dec-2020
https://doi.org/10.1109/WIIAT50758.2020.00039
Dai YWeng JLiu YLiu JChang B(2020)An Approach to Decrease Interference among Modalities in Emotion DetectionJournal of Physics: Conference Series10.1088/1742-6596/1575/1/0120821575(012082)Online publication date: 14-Jul-2020
https://doi.org/10.1088/1742-6596/1575/1/012082
Zhang JZhou YLiu Y(2020)EEG-based emotion recognition using an improved radial basis function neural networkJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-02049-0Online publication date: 7-May-2020
https://doi.org/10.1007/s12652-020-02049-0
Yuan JWu YLu XZhao YQin BLiu T(2020)Recent advances in deep learning based sentiment analysisScience China Technological Sciences10.1007/s11431-020-1634-3Online publication date: 15-Sep-2020
https://doi.org/10.1007/s11431-020-1634-3
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