Sentiment analysis typically involves the following steps:
1. Data Collection: Gathering data from various sources such as social media, news articles, scientific journals, and forums. 2. Preprocessing: Cleaning and organizing the data by removing noise, correcting grammatical errors, and normalizing text. 3. Sentiment Classification: Using machine learning algorithms or lexicon-based approaches to classify the sentiment as positive, negative, or neutral. 4. Evaluation: Assessing the accuracy and reliability of the sentiment analysis model by comparing its predictions with a labeled dataset.
