- 1 Natural Language Processing (NLP)
- 2 Need for Text Mining in NLP
- 3 What is Text Mining?
- 4 What is Natural Language Processing (NLP)?
- 5 Application of NLP
- 6 Terminologies used in Natural Language Processing (NLP)
Natural Language Processing (NLP)
NLP stands for Natural Language Processing, a branch of computer science that comes under Artificial Intelligence. Natural Language Processing (NLP) is a method of communication used by a computer to understand the human language. Language is a channel of communication through which we can read, write, and speak. NLP is a communication channel by which a machine can understand the natural language that is the human language or vice-versa. Before understanding the Natural Language Processing (NLP) in brief, we should know the need for Text Mining because a computer or machine requires structured data. The major part of the daily generating data is unstructured, like human speech. So it’s a big challenge to develop the application of Natural Language Processing (NLP).
Need for Text Mining in NLP
It is needed because of the tremendous amount of data is generating every minute. According to statistics, around 2.5 quintillion bytes of data are created every day, which is only going to increase. With the evolution of communication through social media platforms like Facebook, Instagram, Twitter, and YouTube, we generate so much data every minute. On Instagram, around more than 1.736 million pictures are posted every minute. Similarly, on Twitter, approximately 360k tweets posted every minute.
Now the main problem is with our data generation. Out of all generated data, only 21% of data is structured and well formated while the remaining is unstructured. The primary sources of unstructured data are text messages, Facebook images, and likes, Whatsapp text, comments on Instagram, Emails, etc. The main issue is, what can we do with this unstructured data?
By analyzing and mining the data, we can grow our business and add more value to our firm.
What is Text Mining?
Text mining is defined as the analysis of data available in a day-to-day spoken or written language. Most of the data generated by machines can be used in Text mining, like Word documents, PowerPoint, chat, emails, etc. These all are the sources that help to add grow our business. The data generated from social media, IoT, etc. are mainly unstructured, which can’t be used to improve the business, so we need Text mining.
“Text mining or text analysis is the process to derive high quality or useful information from the natural language text.”
All the data from emails, word, files, documents, etc. are written in natural language text. We use text mining and natural language processing to derive useful insights or patterns from such data.
How Text Mining and NLP are used?
Before knowing the use of text mining, we should understand what text mining is and how it is related to natural language processing. Some so many people are confused about why text mining and NLP are considered as same. So let me clear about the text mining and NLP with the below definition.
Text mining is the process of deriving meaningful information from the natural language text, and NLP is used to process the text mining.
So, Text mining is a vast field that uses the NLP to perform text analysis, and Text mining and NLP is a part of Text mining.
What is Natural Language Processing (NLP)?
Natural language Processing (NLP) is the main component of text mining, which is used to help a machine in reading and analyze the text data. A machine is unknown to English, Hindi, French, etc. It can only interpret data in 0’s and 1’s format. Basically, NLP is a method through which computers and Smartphones understand our language, either written or spoken format. NLP uses the concept of computer science and Artificial Intelligence to study the data and derive useful information from it.
“Natural Language Processing is a part of computer science and Artificial Intelligence (AI) which deals with human language.”
Application of NLP
Before moving the applications of NLP, let’s understand some basic examples where text mining is used.
We all spend a lot of time web surfing but have you ever notice that when you start to type something on Google-like search engine, it shows some suggestions as below.
This feature responsible is known as auto-complete. It automatically suggests for rest of the word for us.
- Spam detection
Apart from this, there is also a term called Spam detection. How does Google help to correct the misspelled words? Below is an example to understand it better.
Now the problem is How Google recognizes the misspelling Amazon prime and shows the keywords that match your misspelling. So, spam detection is also based on the concept of Text Mining and Natural Language Processing (NLP).
- Predictive Typing and spell checker
Further, we have another feature called Predictive typing and spell checkers. Features such as auto-correct, email classification, etc. are the application of NLP and text mining.
- Sentimental Analysis
Sentimental analysis is extremely important in social media monitoring because it helps us to understand the overview of the audience behind a specific topic. So, sentimental analysis is used to understand the public/customer’s opinion on certain products and topics. It is actually an important part of social media platforms as there are almost all social media platforms like Facebook, Twitter, Instagram, etc. use sentimental analysis on a frequent basis.
Chatbots are the solutions for all the consumer frustration regarding customer call assistance. Companies like Pizza Hut, Uber, etc. started chatbots to provide better customer service instead of speech recognition.
- Speech Recognition
NLP is widely used in speech recognition. Alexa, Siri, Google Assistant, and Cortana are the applications of Natural Language Processing (NLP).
- Machine Translation
Machine translation is also an important application of Natural language Processing (NLP). Google translator is the best-suited example of machine translation. It uses NLP to process and translate one language to another.
Other than the above applications, spell checkers, keyword searches, information extraction, advertisement matching are some important applications that use NLP to get useful information from various websites, word documents, files, etc. Advertisement matching is the recommendation of Ads depends on your search history on the web. So, this is all about the applications of NLP, where it is used.
Terminologies used in Natural Language Processing (NLP)
It is the most basic and initial step in Text mining. It is defined as the process of breaking down the data into some smaller chunks or tokens to analyze text mining easily.
How does Tokenization work?
There are some steps to define the process of Tokenization are as follows:
- Step1- Spitting a complex sentence into words.
- Step2- Understand the importance of each individual word with respect to the whole sentence.
- Step3- Produce a structural description of an input sentence.
Example- Let supposes we have a sentence like “Machine Learning is simple to learn.” Now apply tokenization on this given sentence.
First, break this sentence into words e.g., “Machine-learning-is-simple-to-learn.”
Now understand the importance of each word. Now perform the NLP process on each word to understand the importance of each individual word in the given sentence.
Stemming is defined as an algorithm to normalize the words into its root form or base form—this algorithm work by cutting off the suffix or prefix of the word. Stemming has so many limitations, so It can be used in some cases, but not always.
Example- We have different words related to “Reject.”
Lemmatization is a process to overcome the limitation of the Stemming algorithm. It uses morphological analysis or grammar to overcome the limitations.
It is quite similar to Stemming. It maps different words into one common root. In stemming, most of the words cut off like rejection into reject in the previous example. Sometimes, it becomes rej or ject, etc. Due to indiscriminate cutting off the words, it lost its grammatical mean. So that’s why lemmatization was introduced to find out the meaningful word as an output. This is done by morphological analysis or understanding English grammar.
Stop words are defined as a set of commonly used words in any language. If we remove the words that are very commonly used in a given language, we can finally focus on the important words or main keywords. E.g., you are going to search in the search engine “how to make pizza.” Then the search engine shows so many pages as a result containing the term “how to make” rather than pages that show the recipe for making pizza, so you have to disregard these terms. The search engine, like Google, actually focuses on the recipe of making pizza instead of looking for pages on how to make, etc.
The words that are not critically important and also don’t affect the result on a Google-like search engine are called Stop words.
Examples of stop words– how-to, begin, gone, various, and, the, etc.
Also read: List of Stop words
Document Term Matrix
A document term matrix is a matrix with documents designated by rows and words by columns.
In the above example, if you have 1,1,1, then you will get an output as India is great. Similarly, if you have 1,1,0, then you will get an output as India is and so on.