1. What are CNNs and How They Work:

Convolutional Neural Networks (CNNs) were first designed for tasks like recognizing objects in images. In NLP, they’re used to understand text by focusing on local patterns, like specific phrases or combinations of words.

Example:

• Text: “This movie was great!”
• Local Patterns: CNNs focus on phrases like “was great”, understanding their importance in sentiment analysis.

2. How CNNs Process Text:

CNNs use filters (think of them as highlighters) that slide across sentences to extract important features. These filters capture patterns that help in understanding if the text expresses positive or negative feelings.

Example:

• Filter: Looks for phrases like “was great” or “didn’t enjoy”.
• Operation: It scans through the text to find these phrases and their contexts.

3. Using CNNs for Text Classification:

Let’s say we want to classify movie reviews as positive or negative. Here’s how we can set up a simple CNN model using Python and Keras:

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Flatten, Conv1D, MaxPooling1D, Embedding
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences

# Dummy data for illustration
sentences = [
    "This movie was great!",
    "I didn't enjoy the film.",
    "The acting was fantastic.",
    "The plot was confusing."
]
labels = [1, 0, 1, 0]  # 1 for positive, 0 for negative

# Tokenize and pad the sentences
tokenizer = Tokenizer()
tokenizer.fit_on_texts(sentences)
sequences = tokenizer.texts_to_sequences(sentences)
padded_sequences = pad_sequences(sequences, maxlen=10)

# Define the model
model = Sequential()
model.add(Embedding(len(tokenizer.word_index) + 1, 100, input_length=10))
model.add(Conv1D(128, 5, activation='relu'))
model.add(MaxPooling1D(5))
model.add(Conv1D(128, 5, activation='relu'))
model.add(MaxPooling1D(5))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))

# Compile and train the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(padded_sequences, np.array(labels), epochs=5, batch_size=2)

Explanation of the Model:

• Embedding Layer: Converts words into numerical vectors that the model can understand.
• Conv1D Layers: These layers look for patterns (like “was great”) in different parts of the review.
• MaxPooling1D Layers: Helps in summarizing the most important patterns found by the Conv1D layers.
• Dense Layer: Gives the final output, classifying the review as positive or negative.

Real-World Applications:

• Sentiment Analysis: Determining if a review (like for movies or products) is positive or negative based on the words used.
• Text Classification: Sorting news articles or social media posts into different categories like sports, politics, or entertainment.
• Spam Detection: Identifying and filtering out unwanted messages or emails.

Conclusion:

CNNs are great for understanding local patterns and contexts in text, making them useful in many NLP tasks. However, for tasks needing understanding of longer sentences or complex relationships between words, combining CNNs with other methods like RNNs or Transformers can give better results.

Understanding these basics helps in building more intelligent systems that can understand and respond to human language more effectively.