J. Electrical Systems 20-10s (2024):3821-3827 3821 1 Srisudha Garugu 2 Devadi Ganesh 3 Nagadasi Amulya 4 Koyya Avinash 5 K. Prasanna Latha 6 M. Rukmini durga Multimodal Content Analysis and Classification Approach (MCACA) Abstract: - The proliferation of online video platforms has transformed how information is disseminated, offering unprecedented access to a vast array of content. However, this accessibility also brings with it the challenge of identifying and filtering out harmful videos that may contain unethical or inappropriate content. In this work, it presents an advanced method that combines natural language processing (NLP) and machine learning techniques to detect and classify harmful videos effectively. This approach involves converting video content into structured text data, analyzing it using NLP algorithms, and employing machine learning classifiers to categorize videos based on their ethical implications. By leveraging these techniques, it aims to provide a robust solution for identifying and mitigating the impact of harmful content on online platforms. Keywords: Online Video Platforms, Harmful Videos, NLP, Machine Learning, Content Analysis, Classifiers, Ethical Implications I. INTRODUCTION The advent of online video platforms has revolutionized communication, democratizing the dissemination of information and fostering global connectivity. These platforms serve as virtual arenas where individuals from diverse backgrounds share their perspectives, engage in discussions, and showcase their creativity. However, amid this digital landscape, lurks the pervasive issue of harmful content – videos containing morally objectionable, misleading, or explicit material. With the exponential growth of online video consumption, the challenge of identifying and addressing harmful content has become paramount. Such videos, ranging from politically divisive propaganda to graphic violence and hate speech, pose significant risks to individuals' psychological well-being and societal harmony. Consequently, researchers and technologists have intensified efforts to develop advanced methods for detecting and classifying harmful videos. This work explores the Multimodal Content Analysis and Classification Approach (MCACA), a cutting-edge methodology designed to tackle the scourge of harmful online videos. By integrating natural language processing (NLP) and machine learning techniques, MCACA aims to discern the ethical implications of video content and facilitate timely intervention. In recent years, scholars have made significant strides in understanding and combating harmful content proliferation. Studies by Moon et al. (2021) and Shi et al. (2021) underscore the importance of leveraging machine learning algorithms for video classification. Additionally, Gupta et al. (2019) and Chakraborty et al. (2020) highlight the efficacy of multimodal approaches in content analysis, emphasizing the integration of textual and audio features. Moreover, advancements in audio signal processing techniques, as evidenced by the work of Mesaros et al. (2019), have enhanced the accuracy of content classification systems. Graph-based methods proposed by Liu et al. (2020) offer insights into the complex relationships between users and content, aiding in the identification of harmful patterns. As the digital landscape evolves, the development of robust content analysis frameworks is imperative to safeguarding online communities. Through a synthesis of innovative methodologies and interdisciplinary 1 Department of CSE(CSM), ACE College of Engineering, Ankushapur, Telangana, India. Email: srisudha.garugu@gmail.com 2 Department of CSE, GMR Institute of Technology, Vizianagaram, A.P, India. Email: ganesh.d@gmrit.edu.in 3 Department of CSE, Mallareddy Institute of technology and science, Telangana, India. Email: ndamulya6@gmail.com 4 Department of CSE(IoT), Mallareddy engineering College, Telangana, India. Email: avinashkoyya1998@gmail.com 5 Department of CSE, Visakha Institute of engineering and technology, Visakhapatnam, A.P, India. Email: nalliprasanna@gmail.com 6 Department of CSE, Visakha Institute of engineering and technology, Visakhapatnam, A.P, India. Email: rukminidurgakodela@gmail.com Copyright © JES 2024 on-line: journal.esrgroups.org