IES Management College And Research Centre

Unraveling Emotions: Multimodal Deep Learning for Fine-Grained Emotion Recognition (Record no. 54935)

MARC details
000 -LEADER
fixed length control field 02194nam a22001697a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240412155940.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240412b |||||||| |||| 00| 0 eng d
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Riktesh Srivastava
9 (RLIN) 38884
245 ## - TITLE STATEMENT
Title Unraveling Emotions: Multimodal Deep Learning for Fine-Grained Emotion Recognition
300 ## - PHYSICAL DESCRIPTION
Extent 1-6p
520 ## - SUMMARY, ETC.
Summary, etc In the landscape of natural language processing and artificial intelligence, sentiment analysis and emotion recognition hold crucial roles in deciphering human emotions across diverse communication channels. This study addresses a significant research gap by venturing into the promising domain of emotion identification through sentiment analysis, capitalising on the potential of multimodal deep learning. Through an exhaustive literature review, the study seeks to bridge the gap between conventional sentiment analysis methods and the intricate subtleties of human emotions, achieved by fusing data from various modalities. This integration, coupled with fine-grained emotion recognition, strives to heighten the precision of emotion comprehension. Anchored by a robust conceptual framework encompassing variables like information integration, information variability and model type, this research probes the interplay of these factors in shaping emotion consistency. The research methodology involves a two-tailed t-test, a potent statistical tool for hypothesis testing using SmartPLS. The outcomes shed light on the intricate interplay among these variables. While information integration may not exert a significant impact on information consistency, information variability and model type surface as critical factors, each distinctively contributing to the enhancement of information consistency. These insights offer a deeper comprehension of the complexities within this domain, charting a path towards refined insights into the examined relationships.
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term Multimodal Deep Learning
-- Emotion Recognition,
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Riktesh Srivastava
9 (RLIN) 38884
773 0# - HOST ITEM ENTRY
Host Biblionumber 54611
Host Itemnumber 83476
Main entry heading IJBAI
Place, publisher, and date of publication Publishing India Group 2022 New Delhi
Other item identifier 55514106
Title International Journal of Business Analytics and Intelligence
International Standard Book Number 2321-1857
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Journal Article
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Total Checkouts Full call number Barcode Date last seen Price effective from
    Dewey Decimal Classification     Main Library Main Library 12/04/2024   JOURNAL/MGT/55514106JA1 55514106JA1 12/04/2024 12/04/2024

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