Natural Language Processing Techniques, or NLP for short, is one of the most fascinating aspects of modern data science in the present. NLP techniques have revolutionized the way technology could be utilized in the world today. It is also revolutionizing ways for researchers to conduct studies on humans.
As companies start to use technology to spread and process data for their work, like sending e-mails, recording audio, making work documents, spreadsheets, and JSON files, etc., it becomes increasingly more substantial for them to have a great understanding of natural language processing. Interests surrounding the use of natural language processing techniques to increase efficiency in data analysis are growing bigger because of its potential to make our works and lives easier.
In this article, we shall discuss 5 major natural language processing techniques for data scientists and NLP practitioners to use in the conduct of their studies:
- Stemming – in this technique, data, primarily those interpreted in text form, is first cleaned up by transforming words to their infinitive form before data analysts can analyze it. It also removes words that are misspelled and used differently from their actual meaning (like ‘your’ and ‘you’re’).
There are also other stemming algorithms developed to provide data analysts with other ways to achieve grammatical correctness in their works. One such algorithm, and the most common example of one, is the Porter stemmer. Primarily used in the English language, this stemming algorithm process a single word through its five sequential phases to find the root of said word.
If you want to bridge the gap left by the process of stemming in your research analysis, you can also use lemmatization algorithms to make your analysis more grammatically correct. Using words available in its dictionary, this algorithm makes your research data more grammatically correct as it improves on a lot of stemming’s functions.
- Keyword extraction – also known in some cases as keyword detection of keyword analysis, is another text analysis NLP technique that identifies frequently-used terms within text data. As such, this NLP technique is very useful in making summaries of the main ideas and points present within the data.
A common type of keyword extraction technique is the term frequency (TF) and the Inverse Document Frequency (IDF). These applications help in extracting all necessary and important words that come up within the data are reflected in the summary. The TF’s particular job is the typical text mining function keyword extracting algorithms usually have. IDF, meanwhile, does the heavy lifting of putting all these significant words to extract the main idea within the text.
- Named Entity Recognition (NER) – in this NLP technique, it does the same actions that stemming does by extracting text information to classify words within a text. NER helps in quantitative research by classifying the names of people, places, dates, and other classifiable information.
NER does two functions in its algorithm: detecting data and categorizing it to a specific category where it fits. This algorithm is extremely useful in a lot of industries, like in the medical and academic sectors in the country.
- Topic Modelling – this NLP technique serves as an extension to the actions performed by keyword extraction algorithms by identifying the topic as discussed in text data. Only languages used in the library of this NLP can be recognized within this NLP.
There are a lot of topic modeling algorithms known by data analysts in helping them with their research. The most common amongst all of these is the Latent Dirichlet Allocation, where it generates more than one topic from an analyzed text data.
- Text Summarization – In analyzing text data, it is often easier to use a small body of text instead of full-length, uncut data in your research. That is where text summarization algorithms come into play. This NLP technique helps your data from being overly worded by extracting the useful information within a certain text and finding a more logical and concise wording of sentences for its sentences.
There are also other NLP techniques out there that are very reliable in making sure your research data remain relevant in wording to the overall points of your study. Sentiment Analysis, in particular, is another one of those NLP techniques that helps you with your research, particularly in identifying the sentiment of text data through analysis.
These techniques will help you build a reliable and informative research thesis if you understand how to use them properly. If you want and you are capable of doing so, you can use this algorithm in making great research work and you are highly encouraged to do so.