We know that humans are the most intelligent species on our planet. And our path to becoming intelligent has a lot to do with our ability to communicate with each other. There are more than 7000 languages spoken in different parts of the world today. These languages are collectively called human languages or natural language.
Coming to the world of Data Science, around 80% of the data that is available is unstructured and is in the form of text data written in Human Language. This data is collected from sources like Instagram, Facebook, Twitter, etc. So, to analyze this data and understand the insights it provides we need to learn some of the techniques like Text Mining and Natural Language Processing that we can use to analyze the text data.
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Text Mining
Text mining is the process of structuring the input unstructured text, analyzing it, deriving patterns from it, and evaluate it to get meaningful output from it which can be used for solving problems.
Natural Language Processing
Natural Language processing is an interdisciplinary field of computer science and linguistic that deals with the study of communication of machines and humans using natural language. Its main objective is to enable machines to understand human language and develop the ability to communicate using human language about any subjects seamlessly like a human.
Applications of NLP
NLP is one of the most exciting areas of study in the world of Machine Learning. Some of the most interesting applications of NLP are given below:
- Sentiment Analysis
- Chatbots & Virtual Assistants
- Machine Translation
- Speech Recognition
Let’s learn some of the basic tasks done in NLP
Components of NLP
- Tokenization
- Stemming
- Lemmatization
- POS Tagging
- Named Entity Recognition
- Chunking
Tokenization
Tokenization is the first step in NLP. It is the processing of splitting a string into small units or tokens which can be used for further analysis.
Example:
The sentence, “India is my country” can be divided into 4 tokens as:
Stemming
Stemming is the process of converting these different tokens into their root word or their word stem. For example, the stem of the word eating, eaten, eats, etc. is eat.
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