TextCNN
Also known as: CNN — Metin Sınıflandırma (TextCNN), convolutional neural network for sentence classification, sentence-level CNN, TextCNN
TextCNN is a convolutional neural network for text classification, introduced by Yoon Kim in 2014, that applies parallel convolution filters of different window sizes over word embeddings to capture local n-gram patterns. It is fast and effective for sentiment analysis and topic classification.
Key highlights
- Fast to train and effective for sentiment analysis and topic classification.
- Captures local n-gram patterns through parallel filters of several window sizes.
- Position-independent: max pooling picks up the key phrase wherever it appears in the text.
- Leverages pretrained word embeddings to encode meaning without manual feature engineering.
Intuition
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How it works
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When to use it
A good fit for classifying or predicting from labelled text — sentiment analysis or topic classification — when you have a reasonably sized labelled corpus (at least around 200 documents, and more comfortably 500+) and pretrained word embeddings such as Word2Vec or GloVe ready to use. It does not require normally distributed data. On very small text sets (below about 100 documents) deep learning fails and a classical TF-IDF plus machine-learning pipeline is enough; between roughly 100 and 500 documents, gradient boosting is a safer choice because TextCNN tends to overfit.
Strengths & limitations
- Fast to train and effective for sentiment analysis and topic classification.
- Captures local n-gram patterns through parallel filters of several window sizes.
- Position-independent: max pooling picks up the key phrase wherever it appears in the text.
- Leverages pretrained word embeddings to encode meaning without manual feature engineering.
- Needs a labelled text dataset and pretrained word embeddings to perform well.
- Overfits on small corpora: below about 500 documents n-gram patterns are not learned reliably.
- Below roughly 100 documents deep learning fails outright and simpler models are preferable.
- Captures only local windows, so very long-range dependencies across a document are harder to model.
Common pitfalls
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Applications
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Frequently asked
How much labelled text do I need?
TextCNN works best with a few hundred labelled documents or more. Below about 500 it tends to overfit and n-gram patterns are not learned reliably, and below roughly 100 documents deep learning fails outright — a classical TF-IDF plus machine-learning pipeline is then enough.
Do I need pretrained word embeddings?
Pretrained embeddings such as Word2Vec or GloVe are assumed and strongly recommended. They encode word meaning learned from large corpora, which is especially valuable when your labelled set is modest.
What do the different window sizes do?
Each window size lets a filter look at a different length of phrase — for example three, four, or five consecutive words. Running several sizes in parallel captures n-gram patterns of different lengths, and a sensitivity analysis can help choose them.
When should I pick a different method?
On very small text sets (below about 100 documents) use a TF-IDF plus classical machine-learning model; between roughly 100 and 500 documents gradient boosting is a safer choice, since TextCNN overfits when data is scarce.
Sources
- 1.Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. EMNLP.
- 2.Zhang, Y. & Wallace, B. (2015). A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification. arXiv:1510.03820.
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Cite this page
ScholarGate. (2026, June 1). TextCNN. ScholarGate. https://scholargate.app/deep-learning/cnn-text-classification