Multilayer Perceptron (MLP) is a type of neural network that has multiple layers of neurons, where each neuron is connected to all the neurons in the previous and next layers. MLP is a feedforward neural network, which means that the data flows from the input layer to the output layer without any loops or cycles. In this article, we will explain MLP with a real-life example of Sentiment Analysis and provide Python code to implement it.
Sentiment Analysis is the process of analyzing text to determine the sentiment or emotion of the writer. Sentiment Analysis has become a crucial tool for businesses to understand their customers’ opinions about their products or services. Sentiment Analysis can be performed using various techniques, including Rule-Based Systems, Machine Learning, and Deep Learning.
MLP is a popular Machine Learning algorithm that can be used for Sentiment Analysis. MLP consists of three or more layers of neurons: an input layer, one or more hidden layers, and an output layer. The neurons in the hidden layers use an activation function to introduce non-linearity into the network, which enables it to learn complex relationships between the input and output data.
The activation function is a mathematical function that introduces non-linearity into the output of a neuron. The most commonly used activation function for MLP is the Rectified Linear Unit (ReLU) function, which sets all negative values to zero and passes all positive values through unchanged.
The Backpropagation algorithm is used to train the MLP model. Backpropagation is an algorithm that adjusts the weights of the neurons in the network to minimize the difference between the predicted output and the actual output. Backpropagation uses the chain rule of calculus to calculate the derivative of the error with respect to each weight in the network and updates the weights accordingly.
To perform Sentiment Analysis using MLP, we need a dataset of text and its corresponding sentiment labels. The dataset can be preprocessed by removing stopwords, stemming or lemmatizing the words, and converting the text to numerical form using techniques such as Bag of Words or TF-IDF. Once the dataset is ready, we can build the MLP model using libraries such as Keras or TensorFlow.
In Python, we can use the Keras library to implement MLP for Sentiment Analysis. The following steps can be followed to implement MLP for Sentiment Analysis:
Importing Libraries
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from sklearn.model_selection import train_test_split
Loading and Preprocessing Data
df = pd.read_csv('sentiment_dataset.csv')
X = df['text'].values
y = df['sentiment'].values
Defining Model Architecture
model =
Sequential() model.add(Dense(64, input_dim=10000, activation=’relu’)) model.add(Dense(32, activation=’relu’)) model.add(Dense(1, activation=’sigmoid’))
pythonCopy code
### Training and Testing the Model
```python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))
The above code defines an MLP model with two hidden layers, one with 64 neurons and the other with 32 neurons, and an output layer with one neuron that predicts the sentiment label. The input dimension of the model is 10000, which is the size of the vocabulary of the text. The model is trained on the dataset using the binary cross-entropy loss function and the Adam optimizer. The accuracy of the model is measured using the accuracy metric.
In conclusion, MLP is a powerful Machine Learning algorithm that can be used for Sentiment Analysis. MLP consists of multiple layers of neurons and uses an activation function and the Backpropagation algorithm to learn complex relationships between the input and output data. Python provides various libraries such as Keras and TensorFlow to implement MLP for Sentiment Analysis. MLP has its advantages and disadvantages, and its future scope is vast, including fields such as Natural Language Processing, Computer Vision, and Speech Recognition.