Text Classification: How To In Python [Best 2 Ways Machine Learning & Deep Learning]

by | Dec 20, 2022 | Machine Learning, Natural Language Processing

Text classification is an important natural language processing (NLP) technique that allows us to turn unstructured data into structured data; many different algorithms allow you to do this, and so there are also many different implementations available in Python.

This guide covers why classification is valuable and everyday use cases. We then discuss designing a text classification system and the most prominent machine learning and deep learning algorithms. Finally, we provide code examples in scikit-learn and Keras to get you started with your application.

What is text classification?

Text classification is the process of assigning predefined categories to unstructured text data. It is a common task in NLP and can be helpful for various applications, such as sentiment analysis, topic labelling, and spam detection.

There are several ways to perform text classification in Python. One popular method uses machine learning algorithms to learn a classifier from labelled training data. This involves extracting features from the text data and using them to train a classifier, such as a support vector machine (SVM) or a random forest.

random forests classify text in python

AI-generated image of a random forest; random forests classify data into distinct categories.

Why is text classification important?

Text classification is an essential task in natural language processing (NLP) because it allows you to automatically organize and structure text data, extract useful information from it, and use it to solve real-world problems.

Some examples of how text classification can be used include:

  • Sentiment analysis: Classifying text data into positive, negative, or neutral categories based on the sentiment expressed. This can be useful for customer feedback analysis, social media monitoring, and other applications where understanding the sentiment of text data is essential.
  • Spam detection: Classifying text data into spam and non-spam categories based on specific characteristics of the text. This can be useful for filtering out unwanted emails and messages and improving the user experience of email and messaging systems.
  • Topic labelling: Classifying text data into predefined categories based on the topic or subject matter of the text. This can be useful for organizing and searching extensive collections of text data, such as news articles or research papers.

Text classification can be applied to a wide range of text data, including emails, social media posts, customer reviews, etc. It is an essential tool for automating the processing and analysis of large volumes of text data. In addition, it can help organizations and individuals better use their data to solve real-world problems.

Python text classification algorithms

Here are several algorithms that can be used for text classification in Python. Some popular options include:

  1. Support vector machines (SVMs): SVMs are a type of supervised machine learning algorithm that can be used for classification tasks. They work by finding the hyperplane in a high-dimensional space that maximally separates different classes.
  2. Random forests: Random forests are an ensemble learning method that can be used for classification tasks. They work by training many decision tree classifiers and combining their predictions to make a final classification.
  3. Logistic regression: Logistic regression is a type of supervised learning algorithm that can be used for classification tasks. It works by using a logistic function to predict the probability of an example belonging to a particular class and then making a classification based on the predicted probability.
  4. Naive Bayes: Naive Bayes is a type of probabilistic classifier that can be used for classification tasks. It works by using Bayes’ theorem to estimate the probability of an example belonging to a particular class based on the features of the sample.

Choosing the correct algorithm for your specific text classification task is vital based on your data’s characteristics and your application’s requirements. Consider using a combination of different algorithms, such as an ensemble method, to improve the performance of your classifier.

Deep learning algorithms for text classification

Deep learning algorithms can also be used for text classification tasks in Python. Some popular options include:

  1. Convolutional neural networks (CNNs): CNNs are a type of deep learning algorithm particularly well-suited for text classification tasks. They work by applying convolutional filters to the input data to extract features and then using fully connected layers to classify the data based on the extracted features.
  2. Recurrent neural networks (RNNs): RNNs are a type of deep learning algorithm well-suited for text classification tasks because they can process sequential data, such as text. They work by using a looping structure to process the input data one element at a time and using the output of each element as input for the next element.
  3. Long short-term memory (LSTM) networks: LSTM networks are a type of RNN that are particularly well-suited for text classification tasks because they can remember past information and use it to make better predictions. They work by using memory cells and gates to control the flow of information through the network.

To use deep learning algorithms for text classification in Python, you will need to use a deep learning library, such as TensorFlow or Keras. These libraries provide a range of tools and functions that you can use to build and train deep learning models, such as layers, optimizers, and loss functions.

It is important to note that deep learning algorithms can require a large amount of data and computational resources to train and may only sometimes be the best choice for text classification tasks. Therefore, you should consider your data’s specific requirements and characteristics when deciding which algorithm to use.

Steps required for classification

Here are the steps that are typically involved in performing text classification:

  1. Collect and preprocess the text data: This involves gathering the text data you want to classify and preparing it for analysis. This may include cleaning the data by removing irrelevant or noise, standardizing the format, and tokenizing the text into individual words or phrases.
  2. Extract features from the text data: This involves extracting meaningful features from the text data that can be used to train a classifier. This can be done using techniques such as bag-of-words, term frequency-inverse document frequency (TF-IDF), or word embeddings.
  3. Train a classifier: This involves using the extracted features to train a classifier, such as a support vector machine (SVM) or a random forest, using labelled training data. The classifier will learn to predict the class of new text data based on the features of the training data.
  4. Test the classifier: This involves evaluating the classifier’s performance on a holdout dataset to see how accurately it can predict the class of new text data. This can be done using accuracy, precision, and recall metrics.
  5. Use the classifier to classify new text data: Once the classifier has been trained and tested, it can be used to classify new text data by extracting features from the text and using the classifier to predict the class.

These are the general steps that are involved in performing text classification. Still, the specific details will depend on the particular requirements and characteristics of your data and the classifier that you are using.

1. How to implement text classification in Python example

Here is an example of how you can use the scikit-learn library to perform text classification in Python using the CountVectorizer:

# Import the necessary libraries
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.ensemble import RandomForestClassifier

# Define the input data
texts = ['This is a positive text', 'This is a negative text']
labels = [1, 0]  # 1 is positive, 0 is negative

# Extract features from the text data using a CountVectorizer
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)

# Train a classifier using the extracted features
classifier = RandomForestClassifier()
classifier.fit(X, labels)

# Predict the class of a new text
new_text = ['This is a neutral text']
new_X = vectorizer.transform(new_text)
prediction = classifier.predict(new_X)
print(prediction)  
# Output: [1]

This example uses a random forest classifier to classify the text data. Still, you can use any other classifier available in Scikit-Learn, such as an SVM or a logistic regression.

Many other Python libraries and techniques can be used for text classification, such as NLTK and spaCy. It is important to choose the right approach and tools based on the specific requirements and characteristics of your data.

2. How to implement text classification in deep learning example

Here is an example of how you can use a deep learning algorithm for text classification in Python using the Keras library:

# Import the necessary libraries
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Embedding, LSTM, Dense, Dropout
from keras.callbacks import EarlyStopping
from keras.models import Sequential

# Define the input data
texts = ['This is a positive text', 'This is a negative text']
labels = [1, 0]  # 1 is positive, 0 is negative

# Preprocess the text data using a Tokenizer
max_words = 1000
max_len = 150
tokenizer = Tokenizer(num_words=max_words)
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
X = pad_sequences(sequences, maxlen=max_len)

# Build the model
model = Sequential()
model.add(Embedding(max_words, 128, input_length=max_len))
model.add(LSTM(128))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

# Compile and train the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
early_stopping = EarlyStopping(monitor='val_loss', patience=5)
model.fit(X, labels, validation_split=0.2, epochs=10, callbacks=[early_stopping])

# Predict the class of a new text
new_text = ['This is a neutral text']
new_sequences = tokenizer.texts_to_sequences(new_text)
new_X = pad_sequences(new_sequences, maxlen=max_len)
prediction = model.predict(new_X)
print(prediction)  # Output: [[0.5]]

This example uses a long short-term memory (LSTM) network to classify text data into positive and negative categories. The text data is first preprocessed using a Tokenizer to convert the text into numerical sequences that can be fed into the model. The model is then trained using the preprocessed data, and the trained model is used to predict the class of a new piece of text.

You can modify this example by changing the model’s architecture, the preprocessing steps, and the training parameters to suit your specific text classification task. You can also use other deep learning libraries, such as TensorFlow, to build and train deep learning models for text classification in Python.

Closing thoughts

At Spot Intelligence, we often use text classification to turn unstructured data into structured information. Text classification is often considered a supervised learning problem where you provide labelled data, but when dealing with extensive unlabeled data, it’s also practical to use text classification in a semi-supervised manner. If you are interested in learning more about this, let us know in the comments, and we would be happy to create a detailed how-to guide for that as well.

About the Author

Neri Van Otten

Neri Van Otten

Neri Van Otten is the founder of Spot Intelligence, a machine learning engineer with over 12 years of experience specialising in Natural Language Processing (NLP) and deep learning innovation. Dedicated to making your projects succeed.

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