Chatbots are, no doubt, a good advantage for organizations and also their customers. Most people prefer to talk mainly from a chatbox rather than calling the service centers. Facebook released data, and it proved the importance of bots. Every month, about 2 billion messages are being sent between people and companies monthly.
The research made by one of new age B2B marketing agencies revealed that about 71% of people prefer to get customer support from messaging apps. Because it is the fastest medium to get their problems solved without issues, so the future of chatbots in organizations and businesses is bright and colorful.
Chatbots are only an intelligent piece of software that can interact and communicate effectively with people just like humans. Every chatbot is under the NLP (Natural Language Processing) concepts. And also, the NLP is comprised of two things which include:
- NLU (Natural Language Understanding): Machines can understand human language like English.
- NLG (Natural Language Generation): A machine can generate text that is identical to sentences written by a human.
There are various examples of Python Libraries, but written below are six (6) top lists of Python libraries, and they include –
spaCy is a library that is built particularly for developers to develop interactive NLP applications, which can effectively process and ‘understand’ enormous volumes of text. spaCy can also be used to design information extraction or NLU systems, and also pre-process text for deep learning. It is also an open-source library for Natural Language Processing (NLP) in Python language.
spaCy has lots of great features, and some of the features are written below:
Tokenization: This feature assists in segmenting text into words, punctuations, etc.
Part-of-speech (POS) Tagging: This feature assists in designating word types to tokens, as a verb.
Sentence Boundary Detection (SBD): This feature assists in discovering and segmenting single sentences in a text.
Similarity: This feature assists in comparing words, text spans, etc. and pairs the similarities between them.
Text Classification: It assists in designating categories or labels to a document.
Rule-based Matching: It helps in finding and discovering a series of tokens based on their texts and linguistic information.
PyNLPl is commonly pronounced as ‘pineapple,’ which is a Python library for NLP. It can be used for fundamental tasks like the extraction of n-grams and frequency lists, and also to develop an easy and simple language model. This library is divided into diverse packages and modules. It works efficiently on Python 2.7, as well as Python 3.
NLTK is also referred to as the Natural Language Tool Kit. It is an open-source series of libraries and programs for developing programs in Python language. NLTK offers an easy-to-use interface with diverse corpora and lexical resources, like WordNet, with a series of text processing libraries for tokenization, tagging, semantic reasoning, parsing, stemming, and classification.
To install this NLTK, it requires the versions of Python 3.5, 3.6, 3.7, or 3.8. Then, after installing the NLTK package, you need to install the essential datasets and models for certain functions to work effectively.
DeepPavlov is an open-source conversational AI library developed on TensorFlow and Keras. It has all-encompassing and flexible tools that enable developers and NLP researchers to design production-ready conversational skills and complex multi-skill conversational assistants. This library efficiently supports Python 3.6 and 3.7.
ChatterBot is a Python library built to simplify the development of software that helps in engaging in conversation. It also makes use of a selection of machine learning algorithms to create various types of responses which will help in generating chatbots and automate conversations with users.
To install this Chatterbot library, a user needs to make use of pip.
TextBlob is a Python library that is used for processing textual data that is written in Python language. The library offers an uncomplicated API for working into regular NLP tasks, like part-of-speech tagging, noun phrase extraction, sentiment analysis, etc. This library runs effectively on Python versions of 2 and 3, and it pays attention to offering access to normal text-processing operations through a well-known interface.
Presently, about 30% of the tasks are accomplished through the help of chatbots. Some companies make use of the chatbots to offer services such as customer support, generating information, etc. With specific examples such as Siri, Alexa.
So, it’s no longer a myth that chatbot is of a great benefit for humans; it becomes more obvious every day how a chatbot can make a difference in our daily activities.