Sentiment Analysis on Social Media Posts: An AI-Powered Opinion Mining System

The way individuals expressthoughts has changed dramaticallyasaresultofthe explosiverise of digitalplatformslike Facebook,Instagram,YouTube,andTwitter,whichproduceenormousvolumesof user-generated information every day.Analyzing public opinion in areas like politics, economics, enter- tainment, and international issues is made possible by this data.Opinion mining, also known as sentiment analysis,isasubfieldofnaturallanguageprocessing(NLP) thatdividestextintocategorieslikeneutral,neg- ative,andpositive. Conventionalmachinelearningandlexicon-basedmethodshave frequently hadtrouble with casual language, sarcasm, acronyms, and multilingual data. Thisstudysuggestsadeeplearningandtransformer-basedmodels, suchasBERTandRoBERTa,which offer greater semantic comprehension, as part of an AI-driven sentiment analysis framework to address theseissues. Datacollection,textpreparation,featurerepresentation,modeltraining,andevaluationareall partofthesystem. Tokenization, normalization,stopwordelimination,andhandlingofhashtagsandemojis are all handled by preprocessing.For feature extraction, both traditional vectorization methods (TF-IDF) and sophisticated embeddings (Word2Vec, GloVe, and transformer embeddings) are used.Transformer modelsroutinelyoutperformdeep learningtechniqueslike LSTMand conventionalalgorithmslikeRandom Forest, SupportVectorMachines, andLogisticRegression, according tocomparativestudies.Theefficacyof theframeworkisdemonstratedbyexperimentalfindingsshowingimprovedaccuracy,precision,recall,and F1-scores.The suggested approach can be used in real-world fields like marketing, healthcare awareness, political forecasting, and customer experience analysis because of its scalability and versatility.