SpaCy Binary Text Classification Example

Oct 13, 2025 by miodeya.com 21 views

Introduction

Binary text classification is a fundamental task in natural language processing (NLP) that involves categorizing text into two distinct classes, such as spam vs. non-spam emails or personal vs. non-personal information. In this article, we will explore the use of SpaCy, a modern NLP library, for binary text classification. We will discuss the steps involved in preparing the dataset, training a model, and evaluating its performance.

Dataset Preparation

For this task, we have a dataset consisting of two folders: one containing documents related to personal information (e.g., name, email, address, etc.) and the other containing non-personal information. Our goal is to train a model that can accurately classify new, unseen documents into one of these two categories.

Step 1: Data Preprocessing

Before training a model, we need to preprocess the data by converting all text to lowercase, removing special characters and punctuation, and tokenizing the text into individual words or tokens.

import spacy
from spacy import displacy
import pandas as pd
import numpy as np
nlp = spacy.load("en_core_web_sm")
df = pd.read_csv("dataset.csv")
df["text"] = df["text"].apply(lambda x: x.lower())
df["text"] = df["text"].apply(lambda x: ''.join(e for e in x if e.isalnum() or e.isspace()))
df["text"] = df["text"].apply(lambda x: nlp(x).text)

Step 2: Feature Extraction

We need to extract features from the preprocessed text data that can be used to train a machine learning model. One common approach is to use the bag-of-words (BoW) representation, where each word in the vocabulary is associated with a binary feature indicating its presence or absence in the document.

# Create a BoW representation of the text data
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(stop_words="english")
X = vectorizer.fit_transform(df["text"])
y = df["label"]

Step 3: Model Selection and Training

We can now select a suitable machine learning model and train it on the preprocessed data. For binary text classification, a logistic regression model or a support vector machine (SVM) are good choices.

# Train a logistic regression model
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X, y)

Step 4: Model Evaluation

After training the model, we need to evaluate its performance on a held-out test set. We can use metrics such as accuracy, precision, recall, and F1-score to assess the model's performance.

# Evaluate the model on a held-out test set
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))
print("Classification Report:")
print(classification_report(y_test, y_pred))
print("Confusion Matrix:")
print(confusion_matrix(y_test, y_pred))

SpaCy Binary Text Classification Example

Let's consider an example where we have a dataset of two folders: one containing documents related to personal information (e.g., name, email, address, etc.) and the other containing non-personal information. We can use SpaCy to preprocess the text data and extract features using the BoW representation.

# Load the SpaCy English language model
nlp = spacy.load("en_core_web_sm")
df = pd.read_csv("dataset.csv")
df["text"] = df["text"].apply(lambda x: x.lower())
df["text"] = df["text"].apply(lambda x: ''.join(e for e in x if e.isalnum() or e.isspace()))
df["text"] = df["text"].apply(lambda x: nlp(x).text)
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(stop_words="english")
X = vectorizer.fit_transform(df["text"])
y = df["label"]
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X, y)
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))
print("Classification Report:")
print(classification_report(y_test, y_pred))
print("Confusion Matrix:")
print(confusion_matrix(y_test, y_pred))

Conclusion

In this article, we explored the use of SpaCy for binary text classification. We discussed the steps involved in preparing the dataset, training a model, and evaluating its performance. We also provided an example of how to use SpaCy to preprocess the text data and extract features using the BoW representation. By following these steps and using SpaCy, you can build a robust binary text classification model that can accurately classify new, unseen documents into one of two categories.

Future Work

There are several ways to improve the performance of the binary text classification model. Some possible directions include:

Using more advanced feature extraction techniques, such as word embeddings or topic modeling.
Experimenting with different machine learning models, such as random forests or gradient boosting.
Using techniques such as data augmentation or transfer learning to improve the model's performance on unseen data.

Q&A: SpaCy Binary Text Classification

In this article, we will answer some frequently asked questions about SpaCy binary text classification.

Q: What is SpaCy binary text classification?

A: SpaCy binary text classification is a task in natural language processing (NLP) that involves categorizing text into two distinct classes, such as personal vs. non-personal information.

Q: What are the steps involved in SpaCy binary text classification?

A: The steps involved in SpaCy binary text classification are:

Data Preprocessing: Preprocess the text data by converting all text to lowercase, removing special characters and punctuation, and tokenizing the text into individual words or tokens.
Feature Extraction: Extract features from the preprocessed text data that can be used to train a machine learning model. One common approach is to use the bag-of-words (BoW) representation.
Model Selection and Training: Select a suitable machine learning model and train it on the preprocessed data.
Model Evaluation: Evaluate the model's performance on a held-out test set using metrics such as accuracy, precision, recall, and F1-score.

Q: What are some common machine learning models used in SpaCy binary text classification?

A: Some common machine learning models used in SpaCy binary text classification are:

Logistic Regression: A simple and interpretable model that is suitable for binary classification tasks.
Support Vector Machine (SVM): A powerful model that can handle high-dimensional data and is suitable for binary classification tasks.
Random Forest: An ensemble model that combines the predictions of multiple decision trees to improve the model's performance.
Gradient Boosting: An ensemble model that combines the predictions of multiple decision trees to improve the model's performance.

Q: How can I improve the performance of my SpaCy binary text classification model?

A: There are several ways to improve the performance of your SpaCy binary text classification model, including:

Using more advanced feature extraction techniques, such as word embeddings or topic modeling.
Experimenting with different machine learning models, such as random forests or gradient boosting.
Using techniques such as data augmentation or transfer learning to improve the model's performance on unseen data.

Q: What are some common challenges in SpaCy binary text classification?

A: Some common challenges in SpaCy binary text classification include:

Class imbalance: When one class has a significantly larger number of instances than the other class, which can lead to biased models.
Noisy or missing data: When the data contains errors or missing values, which can lead to poor model performance.
High-dimensional data: When the data has a large number of features, which can lead to overfitting.

Q: How can I handle class imbalance in SpaCy binary text classification?

A: There are several ways to handle class imbalance in SpaCy binary text classification, including:

Oversampling the minority class: By creating additional instances of the minority class to balance the data.
Undersampling the majority class: By removing instances from the majority class to balance the data.
Using class weights: By assigning different weights to the classes to balance the data.

Q: How can I handle noisy or missing data in SpaCy binary text classification?

A: There are several ways to handle noisy or missing data in SpaCy binary text classification, including:

Data cleaning: By removing or correcting errors in the data.
Data imputation: By filling in missing values using techniques such as mean or median imputation.
Data augmentation: By creating additional instances of the data using techniques such as rotation or flipping.

Q: How can I handle high-dimensional data in SpaCy binary text classification?

A: There are several ways to handle high-dimensional data in SpaCy binary text classification, including:

Feature selection: By selecting a subset of the most relevant features to reduce the dimensionality of the data.
Dimensionality reduction: By using techniques such as PCA or t-SNE to reduce the dimensionality of the data.
Regularization: By using techniques such as L1 or L2 regularization to prevent overfitting.