When your ML model is trained with AI to automatically classify items according to pre-set categories, you can quickly convert regular browsers into customers.
Text classification process
The text classification process begins with data preprocessing, feature selection, extraction, and classification.
Pretreatment
Tokenization: Text is broken down into smaller, simpler text formats for easy categorization.
standardization: All text in a document must be at the same level of understanding. Some forms of normalization include:
- Maintain grammatical or structural standards throughout the text, such as removing spaces or punctuation. Or, keep lowercase letters throughout the text.
- Removes prefixes and suffixes from a word and brings them back to the original word.
- Removing stop words like ‘and’ ‘is’ ‘the’ adds no value to the text.
Feature Selection
Feature selection is a fundamental step in text classification. This process aims to represent text with the most relevant features. Feature selection helps eliminate irrelevant data and improve accuracy.
Feature selection reduces the input variables to the model by using only the most relevant data and removing noise. Depending on the type of solution you are looking for, you can design an AI model to select only relevant features from text.
Feature extraction
Feature extraction is an optional step that some companies perform to extract additional key features from the data. Feature extraction uses several techniques such as mapping, filtering, and clustering. The main benefit of using feature extraction is that it helps eliminate redundant data and speeds up ML model development.
Tag data into predetermined categories
Tagging text into predefined categories is the final step in text classification. There are three ways to do this:
- Manual tagging
- Rule-based matching
- Learning Algorithms – Learning algorithms can be further classified into two categories such as supervised and unsupervised tagging.
- Supervised learning: ML models can automatically align tags to existing classification data in supervised tagging. If classified data is already available, ML algorithms can map features between tags and text.
- Unsupervised learning: Occurs when there is insufficient existing tagged data. ML models use clustering and rule-based algorithms to group similar text based on things like product purchase history, reviews, personal information, and tickets. By further analyzing these broad groups, you can gain valuable customer-specific insights that can be used to design a tailored customer approach.
There are many different use cases for text classification across industries. Collecting, grouping, classifying and extracting valuable insights from text data has always been used in many fields, but text classification is finding its potential in marketing, product development, customer service, administration and management. This helps companies gain competitive intelligence, market and customer knowledge and make data-driven business decisions.
Developing effective and insightful text classification tools is not easy. Nonetheless, with Shaip as your data partner, you can develop effective, scalable, and cost-effective AI-based text classification tools. We have many accurately annotated, ready-to-use datasets that can be customized to fit the unique needs of your model. We turn your text into a competitive advantage. Contact us today.