1. Data Collection: Gathering textual data from various sources such as social media, customer reviews, emails, etc.
2. Preprocessing: This involves cleaning the data to remove any irrelevant information, such as stop words, punctuation, and special characters.
3. Feature Extraction: Converting text into numerical features that can be analyzed. Techniques like Term Frequency-Inverse Document Frequency (TF-IDF) and word embeddings are commonly used.
4. Model Building: Using machine learning algorithms to build models that can classify, cluster, or predict outcomes based on the textual data.
5. Interpretation: Analyzing the results to derive meaningful insights that can be used for decision-making.