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Introduction
In recent years, deep learning technologies have rapidly advanced and are being applied in various fields.
Among them, Natural Language Processing (NLP) is a technology that enables computers to understand and generate human language,
and is used in various areas such as email classification, sentiment analysis, and machine translation.
This article aims to explain in detail how to classify spam emails using a 1D Convolutional Neural Network (1D CNN).
We will first look at the basics of NLP, then understand the structure and application of 1D CNN, and finally build a spam email classifier through practice.
What is Natural Language Processing (NLP)?
Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that helps machines understand and
interpret natural language. The main tasks in NLP include the following:
- Word Embedding
- Syntax Parsing
- Sentiment Analysis
- Information Extraction
- Language Generation
- Spam Detection
Spam detection is one of the particularly important NLP tasks, as it allows for efficient email management by filtering unwanted emails for users.
Traditionally, such classification tasks have been performed using rule-based approaches or machine learning techniques, but
recently, deep learning technologies have shown high performance in solving these problems.
Introduction to 1D CNN (1-dimensional Convolutional Neural Network)
1D CNN is a neural network structure mainly applied to sequential data, and it is effective in processing one-dimensional data like text data.
CNN is primarily used for image recognition but can also be applied to sequential data. The main components of a 1D CNN are as follows:
- Convolutional Layer: Responsible for feature extraction.
- Pooling Layer: Reduces the dimensionality of the data and decreases computation costs.
- Fully Connected Layer: Outputs the final classification result.
By using 1D CNN, it is possible to efficiently learn local patterns within the text. Therefore, it is suitable for NLP tasks such as spam email classification.
Preparing the Dataset for Spam Email Classification
Various datasets can be used for spam email classification.
For example, the SMS Spam Collection dataset
can be used, and the email dataset includes the Spambase dataset.
These datasets contain emails or messages labeled as spam or non-spam.
To prepare the dataset, you first need to collect the data and proceed through data cleaning and preprocessing steps.
This process includes the removal of special characters and stop words,
text lowercasing, and tokenization.
Text Preprocessing Steps
The first step in building a spam email classification model is to preprocess the text data.
The preprocessing procedure consists of the following steps:
- String Normalization: Converts all characters to lowercase and removes special symbols.
- Tokenization: Splits sentences into words to convert each word into a token.
- Stop Word Removal: Removes words that carry no meaning, such as ‘and’, ‘the’, ‘is’.
- Stemming or Lemmatization: Extracts the base form of words.
After these preprocessing steps, each word must be converted into a vector.
A commonly used method is the Word Embedding technique,
with representative models being Word2Vec, GloVe, and FastText.
This allows words to be represented as vectors in high-dimensional space, with similar-meaning words placed close together.
Model Design and Training
Now, it’s time to design and train the 1D CNN model based on the preprocessed data.
The method to build a spam email classification model using Keras and TensorFlow is as follows:
1. Model Design
The 1D CNN model consists sequentially of convolutional layers, pooling layers, and fully connected layers.
The structure of the model can be defined with the following example code:
from keras.models import Sequential
from keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Embedding, Dropout
model = Sequential()
model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=max_length))
model.add(Conv1D(filters=64, kernel_size=5, activation='relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(10, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
In the above code, the embedding layer performs word embedding,
the convolutional layer extracts features, and the pooling layer reduces dimensions.
Finally, the output layer classifies whether it is spam or non-spam.
2. Model Compilation and Training
To compile and train the model, you need to set the loss function and optimization algorithm.
Generally, for binary classification, the binary_crossentropy loss function is used.
The following code shows how to compile and train the model:
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)
The trained model can be evaluated using a test dataset.
The evaluation results can be checked in terms of accuracy and loss values.
Model Performance Evaluation
To evaluate the model’s performance, we utilize the test dataset.
Commonly, metrics such as F1 Score, Precision, and Recall are used to evaluate the model.
1. Explanation of Evaluation Metrics
- Accuracy: The ratio of correctly classified data among the total data.
- Precision: The ratio of actual positives among those predicted as positive.
- Recall: The ratio of correctly predicted positives among the actual positives.
- F1 Score: The harmonic mean of Precision and Recall.
2. Performance Evaluation Code
The following code shows how to evaluate the model’s performance:
from sklearn.metrics import classification_report
y_pred = model.predict(X_test)
y_pred_classes = (y_pred > 0.5).astype("int32")
print(classification_report(y_test, y_pred_classes))
This allows for a detailed assessment of how well the model performs classification.
Conclusion
In this article, we explored how to classify spam emails using 1D CNN.
We explained the process of building and evaluating a spam email classifier by applying the fundamental technologies
of NLP along with an understanding of deep learning and CNN structures.
These technologies will be useful in solving more complex NLP problems in the future.
We look forward to the innovations that deep learning will bring to the field of artificial intelligence.