Text Classification using CNN
Text classification is the process of categorizing unstructured text into predefined classes or categories using Natural Language Processing(NLP). Text classification is also called as text categorization or text tagging. Some of the Text classification examples include Sentiment Analysis, Spam Detection, News Articles Classification, Topic Detection, and Language Detection. Originally, CNNs were designed and developed for Image classification-related tasks.
CNN for Text Classification
Text data is inherently sequential and high-dimensional due to the large vocabulary involved in language representation. Before delving into CNNs, it’s important to preprocess this data using techniques like tokenization, stemming/lemmatization, and vectorization (e.g., TF-IDF).
The typical architecture for a CNN in NLP includes an embedding layer to convert words into dense vectors, convolutional layers that apply filters over the embedded text, pooling layers (usually max or average) that reduce dimensionality, fully connected layers that interpret the features and finally output layers for classification. Each component plays a vital role in understanding contextual cues within texts.
The architecture of Convolution Neural Networks consists of 3 parallel layers of convolution with word vectors on top obtained from the existing pre-trained model, with 100 filters of kernel sizes 3,4 and 5. It is followed by a dense layer of 64 neurons and a classification layer.
Training CNNs involves feeding it labelled data where each text instance is associated with a specific category. Backpropagation and gradient descent algorithms are used to minimize the loss function, which measures the difference between predicted labels and actual categories. The model learns by adjusting its weights through this iterative process.
Text classification using CNN
Text classification is a widely used NLP task in different business problems, and using Convolution Neural Networks (CNNs) has become the most popular choice. In this article, you will learn about the basics of Convolutional neural networks and the implementation of text classification using CNNs, along with code examples. Also, you’ll learn about CNN Architecture for Text Classification, Implementation steps, use cases and applications.
Table of Content
- Text Classification using CNN
- CNN for Text Classification
- Text classification using CNN Implementation
- Use Cases and Applications
- Challenges and Considerations
- Future Directions
- Conclusion
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