Building a Text Classification Model with Deep Learning

 Step-by-Step Guide to Building a Text Classification Model

1. Define the Problem

Text classification assigns predefined categories to text documents. Examples include:

Spam detection (spam or not)

Sentiment analysis (positive, neutral, negative)

Topic classification (sports, politics, tech, etc.)

2. Collect and Prepare the Dataset

You can use datasets from sources like:

Kaggle

Hugging Face Datasets

Scikit-learn (e.g., 20 Newsgroups)

Example: IMDB Movie Reviews Dataset

from tensorflow.keras.datasets import imdb

from tensorflow.keras.preprocessing.sequence import pad_sequences

# Load dataset

vocab_size = 10000

max_len = 200

(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=vocab_size)

x_train = pad_sequences(x_train, maxlen=max_len)

x_test = pad_sequences(x_test, maxlen=max_len)

3. Preprocess the Text

If you're not using a preprocessed dataset:

Clean the text (remove punctuation, lowercase, etc.)

Tokenize (convert text to integers)

Pad sequences to equal length

Using Keras Tokenizer:

from tensorflow.keras.preprocessing.text import Tokenizer

from tensorflow.keras.preprocessing.sequence import pad_sequences

tokenizer = Tokenizer(num_words=10000, oov_token="<OOV>")

tokenizer.fit_on_texts(texts)

sequences = tokenizer.texts_to_sequences(texts)

padded = pad_sequences(sequences, maxlen=200, truncating='post')

4. Build the Model

Use an embedding layer + a deep learning architecture (like LSTM, GRU, or CNN).

Example: LSTM Model

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout

model = Sequential([

    Embedding(input_dim=vocab_size, output_dim=128, input_length=max_len),

    LSTM(64, return_sequences=False),

    Dropout(0.5),

    Dense(1, activation='sigmoid')  # For binary classification

])

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

model.summary()

5. Train the Model

history = model.fit(

    x_train, y_train,

    epochs=5,

    validation_data=(x_test, y_test),

    batch_size=64

)

6. Evaluate the Model

loss, accuracy = model.evaluate(x_test, y_test)

print(f"Test Accuracy: {accuracy*100:.2f}%")

Optional: Plot accuracy/loss graphs to see model performance over epochs.

7. Make Predictions

predictions = model.predict(x_test)

predicted_classes = (predictions > 0.5).astype("int32")

8. (Optional) Save and Load the Model

# Save

model.save("text_classification_model.h5")

# Load

from tensorflow.keras.models import load_model

model = load_model("text_classification_model.h5")

🧠 Alternatives and Improvements

Use pretrained embeddings (like GloVe or Word2Vec)

Fine-tune transformers (like BERT) for better accuracy

Try data augmentation for small datasets

Implement attention mechanisms

🛠️ Tools and Libraries

TensorFlow / Keras – for building and training models

Scikit-learn – for metrics and preprocessing

NLTK / SpaCy – for natural language preprocessing

Hugging Face Transformers – for state-of-the-art models like BERT

Learn AI ML Course in Hyderabad

Read More

How AI is Enhancing Language Translation Systems

Creating a Sentiment Analysis Model with Machine Learning

How to Use Pre-trained Models for Natural Language Processing

NLP & Text-Based AI

October 03, 2025

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