Chatbots written in Python has gained popularity in the technology and commercial sectors in recent years. These intelligent bots are so good at imitating normal human languages and interacting with humans that corporations from many industries are using them. Everyone is embracing this handy technology to create commercial advantages, from e-commerce enterprises to healthcare organizations. In the following lesson, we will learn about chatbots using the Python programming language and go through how to develop a chatbot in Python.
What is Chatbot?
A chatbot is a conversational application that replaces or augments human support agents with artificial intelligence (AI) and other automation technologies that can converse with end-users through chat.
Chatbot, also known as a chatterbot, is an artificial intelligence (AI) tool that can be incorporated and utilized in any major messaging service.
A chatbot is also known as a “talkbot,” “bot,” “IM bot,” “interactive agent,” and “artificial conversation entity.”
A chatbot is considered one of the best applications of natural language processing.
Do all chatbots use AI?
AI is not used by all chatbots. Some are rule-based, giving highly organized conversations in response to user input. They can only identify queries and remarks that match the keywords chosen by the chatbot’s coder. An artificial intelligence bot, on the other hand, responds to human input via machine learning and natural language processing even when it deviates from a pre-programmed script.
Types of Chatbot
We can categorize the Chatbots into two primary variants: Rule-Based Chatbots and Self-Learning Chatbots.
- Rule-based Chatbots: The Rule-based approach trains a chatbot to answer questions based on a list of pre-determined rules on which it was primarily trained. These set rules can either be pretty simple or quite complex, and we can use these rule-based chatbots to handle simple queries but not process more complicated requests or queries.
- Self-learning Chatbots: Self-learning chatbots are chatbots that can learn on their own. These leverage advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to train themselves from behaviors and instances. Generally, these chatbots are quite smarter than rule-based bots. We can classify Self-learning chatbots furtherly into two categories – Retrieval-based Chatbots and Generative Chatbots.
- Retrieval-based Chatbots: A retrieval-based chatbot works on pre-defined input patterns and sets responses. Once the question or pattern is inserted, the chatbot utilizes a heuristic approach to deliver the relevant response. The model based on retrieval is extensively utilized to design and develop goal-oriented chatbots using customized features such as the flow and tone of the bot in order to enhance the experience of the customer.
- Generative Chatbots: Unlike retrieval-based chatbots, generative chatbots are not based on pre-defined responses – they leverage seq2seq neural networks. This is constructed on the concept of machine translation, where the source code is converted from one language to another language. In the seq2seq approach, the input is changed into an output.
What Was the First Chatbot?
The term “chatbot” first emerged in 1992; nevertheless, the first chatbot is regarded to be ELIZA, a software program developed in the 1960s by MIT scientist Joseph Weizenbaum. ELIZA recognized important words and responded with open-ended inquiries or remarks. The intention at the time was for ELIZA to be used as a type of therapist, listening to people’s issues and responding in a way that made them believe the program understood and empathized with them.
You can still interact with a version of ELIZA here.
Chatbots in the present Generation
We now have intelligent Chatbots powered by Artificial Intelligence that use natural language processing (NLP) to read human commands (text and voice) and learn from experience. Chatbots have become a standard consumer contact tool for businesses and brands with an active online presence (website and social network platforms).
Chatbots, which use Python to communicate with customers, are seen as useful tools. Consider Cortana from Microsoft, Alexa from Amazon, and Siri from Apple. These chatbots are fantastic, aren’t they? It’s a lot of fun to learn how to design a chatbot in Python.
Fundamentally, the Python chatbot is created and developed to take in the data we offer and then analyze it using complicated Artificial Intelligence algorithms. It then provides us with either a written or vocal response. These bots can reply to a wide range of questions and orders because they may learn from their experiences and conduct.
Although chatbots in Python have already begun to dominate the IT scene, Gartner predicts that chatbots will manage nearly 85% of customer-brand interactions by 2020.
Given the growing popularity and use of chatbots in the business, we may boost market value by learning how to construct a chatbot in Python – one of the most widely used programming languages worldwide.
Why Chatbots are important for a Business or a Website
The following are the main reasons why more organizations are embracing the chatbot approach and how they are a win-win formula for acquiring and retaining clients.
- Quick resolution for a complaint or a problem.
- Improve business branding thereby achieving great customer satisfaction.
- Answering questions and answers for customers.
- Making a reservation at a hotel or at a restaurant.
- Save human effort 24×7.
- Enhance business revenue by providing ideas and inspiration.
- Finding details about business such as hours of operation, phone number, and address.
- Automate sales and lead generation process.
- Reduce customer agents waiting time answering phone calls.
Benefits of using Chatbots
- 24×7 availability.
- Instant answers to queries.
- Support multi-language to enhance businesses.
- Simple and Easy to Use UI to engage more customers.
- Cost-effective and user-interactive.
- Avoid communication with call agents thereby reducing the time-consuming tasks.
- Understand the Customer behavior
- Increase sales of a business by offering promo codes or gifts.
Chatbot technology – How do they work?
The most crucial duty of a chatbot is to analyze and detect the purpose of the user’s request in order to extract relevant elements. Following the completion of the analysis, the appropriate answer is sent to the user.
Chatbots operate using one of three categorization algorithms.
Pattern matching
Bots use pattern matching to organize text and provide an appropriate response from clients. AIML (Artificial Intelligence Markup Language) is a standard structured representation of these patterns. A bot can find the correct answer in a comparable pattern. The bots react to anything linked to the connected patterns.
Natural language understanding (NLU)
Natural language understanding (NLU) refers to a chatbot’s capacity to understand human speech. It is the process of transforming language into structured data that can be understood by a machine. NLU adheres to three distinct notions. They are as follows: entities, context, and expectations.
Natural language processing (NLP)
Natural Language Processing (NLP) bots are intended to translate the user’s text or speech inputs into structured data. The information is then utilized to select an appropriate response. Tokenization, chatbot sentiment analysis, entity recognition, and dependency parsing are all crucial processes in NLP.
