Capstone Project: Sentiment Analysis

Duration: 10 min

This module will guide you through building a sentiment analysis model using TensorFlow and Keras. You'll learn how to preprocess text data, build neural networks, and fine-tune your model for optimal performance. This project is crucial for understanding how to apply deep learning techniques to natural language processing tasks.

Text Preprocessing

Text preprocessing is a critical step in sentiment analysis. It involves cleaning the text, tokenizing it into words, and converting words into numerical vectors. Proper preprocessing ensures that the neural network can effectively learn from the text data.

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

# Sample text data
texts = ['I love this product!', 'This is terrible.']

# Initialize Tokenizer
tokenizer = Tokenizer(num_words=1000)

# Fit the tokenizer on the texts
tokenizer.fit_on_texts(texts)

# Convert texts to sequences
sequences = tokenizer.texts_to_sequences(texts)

# Pad sequences to ensure uniform input length
padded = pad_sequences(sequences, padding='post')

print(padded)

Try it in Google Colab:

[[3 4 0]
 [5 6 7]]

Building a Neural Network

Once the text data is preprocessed, you can build a neural network to classify the sentiment. This involves creating layers, compiling the model, and training it on your dataset. A simple neural network for sentiment analysis might include an embedding layer, followed by a dense layer.

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, Dense, GlobalAveragePooling1D

# Define the model
model = Sequential()
model.add(Embedding(input_dim=1000, output_dim=64))
model.add(GlobalAveragePooling1D())
model.add(Dense(1, activation='sigmoid'))

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

# Summary of the model
model.summary()

💡 Tip: Ensure that your input data is properly padded and that the vocabulary size in the Embedding layer matches the number of unique words in your Tokenizer.