Sentiment classification typically involves several steps:
Data Collection: Gathering text data from various sources such as social media, customer reviews, and surveys. Text Processing: Cleaning and preparing the text data for analysis, which may involve tokenization, removing stop words, and stemming. Feature Extraction: Converting text into numerical features using techniques such as TF-IDF or word embeddings. Model Training: Using machine learning models such as Naive Bayes, Support Vector Machines (SVM), or deep learning models to classify the sentiment. Evaluation and Tuning: Assessing the model's performance and fine-tuning it for better accuracy.