Have you ever wanted to master NLP? If so, I have five techniques that will change your life! In the last few decades, computers able to understand and process natural language. As a result, many new applications can leverage this technology for more accurate processing of text data.
One of these is Natural Language Processing (NLP). NLP has become an essential part of our lives as it allows us to talk with machines in a way they understand. This blog post will discuss five NLP techniques every data scientist should know.
- A technique that breaks up sentences into individual words or word tokens.
- It is the first step in text processing as it gives us a way to deal with each word individually.
- Tokenization is either done by splitting up an input string into words or groups of the word. Depending on the application, you might choose one over the other.
- For example, splitting words would be the best approach to find new misspelled versions of a known word.
- Stemming is a method that reduces words to their root. It allows us to deal with variations of a comment by using its root form instead.
- For example, "running," "runs," and "ran" would all be reduced to the stem word "run." Stemming algorithms share the same purpose: to remove the grammatical additions of words to get their root form.
- It allows for automatic text simplification, which is essential when condensing the input data into a single searchable string.
- Lemmatization is a process that reduces inflected words to their base or dictionary form.
- For example, reduction of "walked," "walking," and "walk" to the root word walk.
- Lemmatization is stemming done right. Stemming reduces words to their root forms, but it does not take into account morphological rules. On the other hand, Lemmatization builds up word knowledge, which allows for base or uninflected word matching.
4) Keywords Extraction:
- This process finds the most important words when applied to text, phrases, or sentences.
- Keywords extraction means finding essential words in a given sentence, and this gets done by using TF-IDF (Term Frequency-Inverse Document Frequency).
5) Sentimental Analysis:
- Sentiment analysis is a text mining technique that has applications in many fields.
- It can also be helpful when building chatbots as word sentiment can give us an idea of what the user is saying.
- Sentimental Analysis helps identify emotional, social, or opinionated aspects within written language.
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