When the ML model is trained on AI that automatically categorizes articles into predefined categories, you can quickly convert casual browsers into customers.
Text classification process
The text classification process begins with preprocessing, feature selection, data extraction and classification.
Pre-treatment
Tokenization: The text is divided into smaller, simpler text shapes for easy classification.
Normalization: All text in a document must be at the same level of understanding. Some forms of standardization include,
- Maintain grammatical or structural standards throughout text, such as removing white space or punctuation. Or by keeping lowercase letters throughout the text.
- Remove prefixes and suffixes from words and return them to their root word.
- Remove stop words such as “and” “is” “the” and many others that do not add value to the text.
Feature Selection
Feature selection is a fundamental step in text classification. The process aims to represent texts with the most relevant characteristics. Feature selections help remove irrelevant data and improve accuracy.
Feature selection reduces the input variable to the model by using only the most relevant data and eliminating noise. Depending on the type of solution you are looking for, your AI models can be designed to choose only relevant features from text.
Feature extraction
Feature extraction is an optional step that some companies undertake to extract additional key features in the data. Feature extraction uses several techniques, such as mapping, filtering, and clustering. The main advantage of using feature extraction is that it helps to remove redundant data and improve the speed of ML model development.
Tagging data into predetermined categories
Tagging text into predefined categories is the final step in text classification. This can be done in three different ways,
- Manual marking
- Rules-based matching
- Learning Algorithms – Learning algorithms can further be classified into two categories such as supervised marking and unsupervised marking.
- Supervised learning: The ML model can automatically align tags with existing categorized data in the supervised markup. When categorized data is already available, ML algorithms can map function between tags and text.
- Unsupervised learning: This occurs when there is a shortage of previously existing labeled data. ML models use clustering and rule-based algorithms to group similar texts, for example based on product purchase history, reviews, personal information and tickets. These large groups can be analyzed in more detail to derive customer-specific insights that can be used to design tailored customer approaches.
There are several use cases for text classification across industries. Although collecting, grouping, classifying and extracting valuable information from text data has always been used in several fields, text classification finds its potential in marketing, product development, customer service , management and administration. It helps businesses gain competitive intelligence, market and customer insight, and make data-driven business decisions.
Developing an effective and insightful text classification tool is not easy. However, with Shaip as your data partner, you can develop an efficient, scalable and cost-effective AI-based text classification tool. We have tons of Precisely annotated, ready-to-use datasets which can be customized for your model's unique requirements. We transform your text into a competitive advantage; contact us today.