Natural Language Processing (NLP) consists of developing applications and services capable of understanding human languages. Some practical examples of NLP are speech recognition, for example, Google voice search, understanding content or analyzing feelings, etc.
Use of Natural Language Processing (NLP) in Machine Learning
Nowadays, most of us have smartphones with voice recognition. These smartphones use NLP to understand what is being said. Besides, many people use laptops whose operating system has built-in speech recognition like Cortana.
In this article, I will take you through some very common uses of Natural Language Processing (NLP), by practising these applications of NLP with Machine Learning you can easily master yourself with working on the tasks of NLP.
Named Entity Recognition (NER)
Named entity means anything that is a real-world object such as a person, place, organization, product that has a name. For example – “My name is Aman and I and a machine learning trainer”. In this sentence, the name “Aman”, the field or subject “Machine Learning” and the profession “Trainer” are named entities.
In machine learning, named entity recognition (NER) is a task of natural language processing (NLP) to identify named entities in a certain piece of text. You can learn the practical implementation of Named Entity Recognition from here.
You must have seen a cloud filled with words in many analysis tasks and machine learning projects. A WordCloud represents the importance of each word in a set of words by analyzing the frequency of terms.
The use of WordCloud is primarily in natural language processing which is an area of artificial intelligence. The idea behind this is that it will represent the most used words in a paragraph, website, social media platforms or even in Speech to highlight the main purpose of the article. You can learn the practical implementation of WordCloud from here.
NLP For WhatsApp Chats
You can also use NLP you analyse the chats between a group or two people. If you have never exported your WhatsApp chats before, don’t worry it’s very easy. For NLP of WhatsApp chats, you need to extract the WhatsApp chats from your smartphone. You just need to open any chat in your WhatsApp then select the export chat option. You can learn the implementation of NLP for WhatsApp Chats from here.
Sentiment Analysis using NLP
Sentiment analysis is the interpretation and classification of emotions (positive, negative, and neutral) in text data using text analysis techniques. Sentiment analysis tools allow businesses to identify customer sentiment towards products, brands or services in online reviews. You can learn the practical implementation of Sentiment Analysis from here.
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