Sentiment analysis involves analyzing text to understand the sentiment or emotion behind it.

It is a branch of NLP that utilizes computational techniques to determine the sentiment of text data.

Sentiment analysis can be applied to various forms of text, including social media posts, customer reviews, news articles, and more.

The main objective of sentiment analysis is to classify text into positive, negative, or neutral sentiment categories.

Sentiment analysis algorithms often use machine learning techniques to learn patterns and features from labeled training data.

The most popular sentiment analysis algorithms are SVM, Naive Bayes, RNNs, etc.

It is widely used in social media monitoring to understand user sentiment towards brands, products, or events.

Sentiment analysis can help in predicting stock market trends by analyzing news or social media discussions.