Sentiment analysis is a natural language processing task that involves identifying the sentiment of a piece of text, such as whether it is positive, negative, or neutral. This can be useful for a variety of applications, such as understanding customer feedback, detecting fake news, and classifying social media posts.
In this article, we will show you how to perform sentiment analysis using Keras and Tensorflow. We will use the IMDB dataset, which contains movie reviews that have been labeled as positive or negative.
Data preparation
The first step is to prepare the data. We will use the `tf.keras.datasets.imdb` module to load the data. This module provides us with a training set and a test set. The training set will be used to train our model, and the test set will be used to evaluate its performance.
Each review in the dataset is a sequence of words. We need to convert these sequences into vectors so that we can feed them into our model. We can do this using the `tf.keras.layers.TextVectorization` layer. This layer will create a vocabulary of all the words in the dataset, and it will map each word to a unique integer.
Model building
Once the data is prepared, we can start building our model. We will use a simple neural network with two layers. The first layer is an `Embedding` layer, which will convert the sequences of words into vectors. The second layer is a `Dense` layer, which will classify the reviews as positive or negative.
model = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_dim),
tf.keras.layers.Dense(1, activation='sigmoid')
])
The `vocab_size` parameter is the size of the vocabulary, and the `embedding_dim` parameter is the size of the embedding vectors.
Training
Now that we have built our model, we can train it. We will use the `fit` method to train the model on the training set. The `fit` method takes several parameters, including the number of epochs, the batch size, and the learning rate.
model.fit(train_dataset, epochs=10, batch_size=64)
The `epochs` parameter specifies the number of times that the model will go through the training set. The `batch_size` parameter specifies the number of reviews that will be processed at a time. The `learning_rate` parameter specifies how quickly the model will learn.
Evaluation
Once the model is trained, we can evaluate its performance on the test set. We can do this using the `evaluate` method. The `evaluate` method returns the loss and accuracy on the test set.
loss, accuracy = model.evaluate(test_dataset)
The `loss` parameter is the average loss on the test set. The `accuracy` parameter is the percentage of reviews that were correctly classified.
Conclusion
In this article, we have shown you how to perform sentiment analysis using Keras and Tensorflow. We have used the IMDB dataset to train a simple neural network that can classify movie reviews as positive or negative. The model achieved an accuracy of 86% on the test set.
You can use this same approach to perform sentiment analysis on other types of text data. For example, you could use it to classify customer feedback, detect fake news, or classify social media posts.
