Processing Multimodal Information

Challenges and Solutions for Multimodal Sentiment Analysis and Hate Speech Detection

verfasst von

Sherzod Hakimov, Gullal S. Cheema, Ralph Ewerth

Abstract

This chapter explores the challenges and solutions for processing multimodal information, specifically in the context of multimodal sentiment analysis and hate speech detection. The increasing amount of multimodal data, such as text, images and videos, presents unique challenges for machine learning algorithms. These challenges include the integration and fusion of information from multiple modalities to acquire the overall context. In this chapter, first, we present an overview of recent developments on multimodal learning techniques in the context of sentiment and hate speech detection; second, we present a multimodal model that combines different visual aspects and features for multimodal sentiment detection; and third, we present a multi-task multimodal model for misogyny detection in multimodal memes.

Organisationseinheit(en)

Forschungszentrum L3S

Externe Organisation(en)

Universität Potsdam
Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek

Typ

Beitrag in Buch/Sammelwerk

Seiten

71-94

Anzahl der Seiten

24

Publikationsdatum

2025

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Allgemeine Computerwissenschaft, Allgemeine Sozialwissenschaften

Ziele für nachhaltige Entwicklung

SDG 5 – Gleichberechtigung der Geschlechter

Elektronische Version(en)

https://doi.org/10.1007/978-3-031-64451-1_4 (Zugang: Offen)