Harnessing Natural Language Processing for Sentiment Analysis in Content Moderation
Keywords:
Natural Language Processing, Sentiment Analysis, Content Moderation, Text Classification, Machine Learning, Deep Learning, Ethical AI, Multilingual Analysis, Author Name, Scopus, Springer, Journal Name, Wissira, Journal Short Form, Wissira Press, Wissira Research Lab, Research Gate, SSRN, ISSN, Academia, UGC Care, PubMed, WOSAbstract
Natural Language Processing (NLP) has emerged as a pivotal technology in automating and improving content moderation across various digital platforms. Sentiment analysis, a subset of NLP, aids in identifying harmful or inappropriate content by analyzing textual data's emotional tone and intent. This study explores the application of sentiment analysis for effective content moderation, focusing on algorithmic approaches, datasets, and real-world implementation challenges. A hybrid model leveraging deep learning and rule-based systems is proposed to enhance accuracy in detecting harmful sentiments. The study also examines the implications of contextual nuances, multilingual challenges, and ethical concerns, concluding with potential advancements to address these issues.
