AI analyzes social media sentiment by first collecting vast amounts of public data from various platforms. This data undergoes preprocessing, which involves cleaning text, removing emojis or irrelevant characters, and converting text into a machine-readable format. Next, Natural Language Processing (NLP) techniques are applied, including tokenization, lemmatization, and part-of-speech tagging, to break down sentences and understand their linguistic structure. Machine learning models, such as Support Vector Machines (SVM), Naïve Bayes, or more advanced deep learning architectures like Recurrent Neural Networks (RNNs) or Transformers, are then trained on large datasets labeled with positive, negative, or neutral sentiments. These models predict the emotional tone and subjectivity of a post, often providing a score for different sentiments. This enables a granular analysis of public opinion on topics, brands, or events by identifying patterns and shifts in sentiment over time. More details: https://gals.graphis.ne.jp/mkr/out.cgi?id=01019&go=https://abcname.com.ua/