This page provides an example of how text classification can be implemented. Text classification is a common task in natural language processing (NLP) where the goal is to categorize text into predefined classes.
Basic Concept
Text classification is used in various applications such as sentiment analysis, spam detection, and topic classification. The process involves training a model on a labeled dataset and then using the model to predict the class of new, unseen text.
Example Dataset
Consider a dataset containing product reviews. The dataset is labeled with positive and negative sentiments.
- Positive sentiment: "I love this product!"
- Negative sentiment: "This product is terrible!"
Model Training
To train a text classification model, we can use techniques such as Naive Bayes, Support Vector Machines, or neural networks. For this example, let's use a simple Naive Bayes classifier.
Model Evaluation
After training the model, we evaluate its performance using metrics such as accuracy, precision, recall, and F1 score.
Application
Once the model is trained and evaluated, it can be used to classify new reviews into positive or negative sentiment.
More Resources
For further reading on text classification, check out our Text Classification Tutorial.