The process of text mining involves several steps:
Data Collection: Gathering relevant text data from various sources such as research papers, patents, and online databases. Preprocessing: Cleaning and preparing the text data by removing noise, tokenization, stemming, and lemmatization. Feature Extraction: Converting text into numerical features using techniques such as TF-IDF (Term Frequency-Inverse Document Frequency) or word embeddings. Analysis: Applying machine learning algorithms and statistical methods to identify patterns, trends, and insights. Visualization: Presenting the results through graphs, charts, and other visual tools for better interpretation.
