<![CDATA[Social media generates enormous volumes of text data, creating both opportunities and challenges for analysis. Natural Language Processing (NLP) enables in-depth analysis of public opinion, identification of trends and language patterns from social media texts. However, texts from social media often face problems with informal language, slang, and spelling errors. This research discusses the application of NLP techniques, such as sentiment analysis, tokenization, and text classification, and compares classical machine learning models (Naive Bayes and SVM) with deep learning models (BERT). Results show deep learning-based models excel at understanding informal language contexts, producing more accurate analysis. This study makes an important contribution in the development of AI-based applications for social media analysis.]]>
Copyrights © 2025
