Different Methods for Sentiment Analysis:

Supervised Learning: In supervised learning, sentiment analysis is treated as a task of classification. On labeled datasets, every text has a sentiment label associated with it (positive, negative, or neutral). This allows models to be trained.

Unsupervised Learning: Without the need for labeled training data, unsupervised techniques use topic modeling or clustering to put comparable sentiments in one group.

Methods for Deep Learning: Recurrent Neural Networks (RNNs): While they may struggle with long-term relationships, RNNs are excellent at capturing sequential dependencies in text.

Convolutional Neural Networks (CNNs): CNNs are useful for sentiment analysis tasks because they are good at identifying subtle patterns in text.

Transformers: By recognizing word associations and capturing contextual information, transformer-based models—like BERT and GPT—have produced state-of-the-art sentiment analysis results.