Text classification is a fundamental task in natural language processing (NLP) that involves categorizing text into predefined classes or categories. It is widely used in various applications such as sentiment analysis, spam detection, and topic classification. In this project, we explore the techniques and methodologies behind text classification.

Techniques Used

  1. Bag of Words (BoW): This technique converts text into a numerical vector representation by counting the frequency of each word in the text.
  2. Term Frequency-Inverse Document Frequency (TF-IDF): This technique is an extension of the BoW model and considers the importance of words in a document by incorporating the frequency of words across all documents.
  3. Word Embeddings: Techniques like Word2Vec and GloVe convert words into dense vectors that capture semantic meaning.
  4. Deep Learning Models: Models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have shown promising results in text classification tasks.

Implementation

We implemented a text classification model using Python and TensorFlow. The dataset used for training and testing the model was the IMDB movie reviews dataset, which contains 50,000 movie reviews labeled as positive or negative.

import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

# Tokenize the text
tokenizer = Tokenizer(num_words=10000)
tokenizer.fit_on_texts(data)

# Convert text to sequences
sequences = tokenizer.texts_to_sequences(data)

# Pad sequences
padded_sequences = pad_sequences(sequences, maxlen=256)

# Build the model
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(input_dim=10000, output_dim=16, input_length=256),
    tf.keras.layers.Conv1D(128, 5, activation='relu'),
    tf.keras.layers.MaxPooling1D(),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

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

# Train the model
model.fit(padded_sequences, labels, epochs=10, batch_size=32)

Results

The model achieved an accuracy of 85% on the test set, which is a good result considering the complexity of the task.

Text Classification Results

Further Reading

For more information on text classification, you can refer to the following resources:

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