Text Preprocessing
In the first stage, we begin by collecting data from multiple sources and building a raw text corpus. Irrelevant, damaged, or incomplete data is removed, and valuable text is normalized and prepared for further analysis.
Text Parsing and Exploratory Data Analysis
This is the structuring stage, as the raw data is filtered and organized to do a more focused analysis with a modest dataset. This involves recognizing and eliminating irrelevant sections, extracting coded metadata, and determining the format. By choosing the different intents and entities required for the predetermined tasks, a deep exploratory analysis builds up a format for representation.
Text Representation and Transformation
Now that the datasets are categorized, we utilize several visualization techniques to represent the data in a more meaningful format to retrieve useful insights. This includes a syntactic, semantic, and pragmatic analysis of the content to get an overview of the interpretable content.
Modeling
Text mining at this stage assists with funneling down the data and doing targeted information retrieval.
Evaluation & Deployment
At the last stage, the NLP model is tested for performance against various training parameters. The metrics are analyzed, and corrective measures are taken wherever required. The successful model is deployed in the execution environment.