Digitization is transforming society into a mobile-first population. As messaging applications grow in popularity, chatbots are increasingly playing an important role in this mobility-driven transformation. Chatbots allow businesses to connect with customers in a personal way without the expense of human representatives. Chatbots have become popular as a time and money saver for businesses and an added convenience for customers.
The project aims to have a fully functional personal assistant, making the world a more efficient and connected place to live and work.
Chatbots have come a long way , but building a Chatbot using Natural Language Processing would be a challenging task. Chatbot is a computer program that simulates and processes human conversation, allowing humans to interact with digital devices as if they were communicating with a real person.
Chatbot could be implemented and be perfect for :
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Delivering more natural experiences with a virtual agents.
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Provides rich and flexible interactions as they happen in the real world.
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Improve customer engagement.
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Technological advantages to stay competitive in the market.
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Reduces cost and boost operational efficiency.
Interesting applications of chatbots in various industries:
- Travel such as Book Flights
- Health Care
- E-commerce
- Education
- Finance
Here we will be using libraries like JSON, Natural Language Toolkit (NLTK), pickle for serialization, Numpy, TensorFlow and Keras.
Tokenization means to split up the text into individual words.
Lemmatizer we will lemmatize the individual words by creating a lemmatizer. Lemmatizer reduces the words to its stem or lemma.
Sequential Model a Deep Learning model from Keras. Sequential model is being used to build the neural network.
The Sequential model is a linear stack of layers. It allows us to specify a neural network, precisely, sequentially from input to output, passing through a series of neural layers, one after the other.
- Input Layer that is a Dense Layer with 128 & 64 neurons and input shape dependent on the size of the training data and activation function is Rectified Linear Unit (ReLU).
- Dropout Layer that randomly drops 50% of the previous layer's outputs during training to further reduce overfitting.
- Dense Layer with the number of neurons equal to the number of classes in the output layer.
- Softmax activation function to obtain the probability distribution of the classes.
We create a Chatbot application that uses the trained model. Four different functions are used:
- Cleaning up sentence This function returns the preprocessed sentence as a list of words.
- Getting Bag of Words The second function is used for converting a sentence into a bag of words that is a list full of 0’s and 1’s that indicates that the word is there or not. It returns the bag of words vector as a Numpy array.
- Predicting the classes from sentences Returns the list of tuples as the predicted classification for the input sentence.
- Function to get response The response() method is used to predict the intent of the input sentence and then returns a response based on the predicted intent.
Finally we can run the Chatbot and we will be able to chat with the Chatbot.
The expected outcome of this project is to build a Chatbot. Following a logical process, with neural networks, and limited amount of lines of code, we can understand the natural language very well for Chatbot environments.
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Chatbot helps in enhancing the processes and elevates experiences to the next level while also increasing the overall growth and profitability.
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Technological advantages to stay competitive in the market.
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It saves time, effort, and costs that further leads to increased customer satisfaction and increased engagement in any business.