#020: Sentiment Analysis with TensorFlow.js and Node.js
##Introduction
In this post, we'll be covering how to do sentiment analysis with TensorFlow.js and Node.js. Sentiment analysis is a process of computationally determining whether a piece of writing is positive, negative, or neutral. It's often used to gauge public opinion on social media, and has a wide range of other applications.
We'll be using the TensorFlow.js library, which is a JavaScript library for training and deploying machine learning models. TensorFlow.js can be used in a Node.js environment, which we'll be using for our sentiment analysis.
##Getting Started
Before we get started, there are a few things you'll need to set up. Firstly, you'll need to have Node.js installed on your machine. You can download it from the Node.js website.
Once you have Node.js installed, you'll need to install the TensorFlow.js library. You can do this with the following command:
npm install @tensorflow/tfjs
Next, you'll need to create a new file for our sentiment analysis program. We'll call it sentiment.js
. You can do this with your favorite text editor.
##Initializing the TensorFlow.js Environment
The first thing we need to do in our sentiment.js
file is to initialize the TensorFlow.js environment. We can do this with the following code:
const tf = require('@tensorflow/tfjs');
This will load the TensorFlow.js library into our program so that we can use it.
##Loading the Dataset
Now that we have the TensorFlow.js environment set up, we need to load the dataset that we'll be using for training our sentiment analysis model. We'll be using the IMDB movie review dataset. This dataset contains 50,000 movie reviews, each with a label of 0 (negative) or 1 (positive).
We can load this dataset into our program with the following code:
const dataset = tf.data.csv('https://storage.googleapis.com/tfjs-datasets/imdb_reviews.csv');
This will load the dataset into a dataset
variable.
##Preprocessing the Dataset
Before we can use the dataset for training, we need to preprocess it. This involves splitting the dataset into training and testing sets, and converting the text data into numerical vectors.
We can do this with the following code:
const [train, test] = dataset.split(0.8);
const vectorizer = new tf.layers.Embedding(10000, 16);
const trainX = train.map(example => vectorizer.apply(example.text));
const testX = test.map(example => vectorizer.apply(example.text));
const trainY = train.map(example => example.label);
const testY = test.map(example => example.label);
This code will split the dataset into training and testing sets, and then vectorize the text data. The vectorized data will be stored in the trainX
and testX
variables for the training and testing sets, respectively. The labels for the training and testing sets will be stored in the trainY
and testY
variables, respectively.
##Building the Model
Now that we have our dataset preprocessed, we can build the model that we'll be using for sentiment analysis. We'll be using a simple neural network with one hidden layer.
We can build the model with the following code:
const model = tf.sequential();
model.add(tf.layers.dense({ units: 16, activation: 'relu', inputShape: [16] }));
model.add(tf.layers.dense({ units: 1, activation: 'sigmoid' }));
model.compile({ loss: 'binaryCrossentropy', optimizer: 'adam' });
This code will create a new sequential
model, and then add a hidden layer and an output layer to the model. The hidden layer will have 16 units, and the output layer will have one unit. The model will be compiled with the binaryCrossentropy
loss function and the adam
optimizer.
##Training the Model
Now that we have our model built, we can train it on our dataset. We'll train it for 10 epochs, which means that the model will see the training data 10 times.
We can train the model with the following code:
model.fit(trainX, trainY, { epochs: 10 })
.then(() => {
// evaluate the model on the test set
});
This code will train the model on the training set, and then evaluate it on the test set.
##Evaluating the Model
Once the model has been trained, we can evaluate its performance on the test set. We'll use the model.evaluate()
method to do this.
We can evaluate the model with the following code:
model.evaluate(testX, testY)
.then(results => {
// log the results
});
This code will evaluate the model on the test set and print the results.
##Making Predictions
Now that we have a trained model, we can use it to make predictions on new data. To do this, we'll use the model.predict()
method.
We can make predictions with the following code:
const prediction = model.predict(tf.tensor2d([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]]));
prediction.print();
This code will make a prediction on the data [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
. The print()
method will print the prediction to the console.
##Conclusion
In this post, we covered how to do sentiment analysis with TensorFlow.js and Node.js. We covered how to load and preprocess the dataset, build the model, train the model, and make predictions with the model.