How to Get Started with NLP Using Python?
Take a look at the information on how to start NLP using Python
The goal of natural language processing (NLP) is to make computers capable of comprehending and processing human languages. Working with structured data, like spreadsheets, is a breeze for computers. However, we write and speak a lot of unstructured information.
NLP aims to enable computers to comprehend unstructured texts and extract meaningful information from them. Thanks to open-source libraries of Python like spaCy and NLTK, many techniques of NLP using Python are implemented with just a few lines of code.
Here are the ways to get started with NLP using Python:
- One of the most well-known NLP methods is sentiment analysis, in which a piece of text—such as a comment, review, or document—is examined to determine whether the data is positive, negative, or neutral. It can be used in a lot of places, like banking, customer service, and healthcare.
- Named Entity Recognition, or NER is a method for locating and categorizing named entities in text into categories like people, organizations, locations, expressions of time, quantities, monetary values, percentages, and so on. It is used to improve content classification, customer service, search engine algorithms, and recommendation systems, among other things.
- Lemmatization and stemming are two commonly used NLP techniques.
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A word is normalized in two distinct ways.
- Branching: It reduces a word to its root. For instance, the term “friend” will replace the words “friendship” and “friendship.” Stemming might not provide us with a grammatical word from a dictionary for a particular set of words.
- Lexiconing: Lemmatization, in contrast to stemming, finds the dictionary word rather than truncating the original word.
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Because lemmatization algorithms extract the correct lemma from each word, they frequently require a language dictionary to correctly classify each word.
Both methods are widely used, so you should choose one based on the objectives of your project. Lemmatization is slower to process than stemming, so stemming is a good choice if speed rather than accuracy is the project’s objective; however. Lemmatization is an option to consider if accuracy is important.
- The Bag of Words (BoW) model is a way to represent text in the form of fixed-length vectors. We can use this to represent text in numbers for machine learning models. The model is only concerned with the text’s word frequency and does not care about the order of the words.
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It can be used in NLP, document classification, and information retrieval from documents.
- Term Frequency–Inverse Document Frequency (TF-IDF):
In contrast to the Count Vectorizer, the TF-IDF calculates “weights” that indicate a word’s relevance to a document in a corpus (a collection of documents). The number of documents in the corpus that contain a word offsets an increase in the TF-IDF value that is proportional to the number of times the word appears in the document. Simply put, the value or rarity of a term is proportional to its TF-IDF score. It can be used in information retrieval in the same way that search engines aim to provide results that are most relevant to what you’re looking to do.
- Wordcloud:
Wordcloud is a well-liked method for locating keywords in a text. The font size of words with a higher frequency in a word cloud is larger and bolder than that of words with a lower frequency. With the word cloud library and the style cloud library, you can create simple and attractive Python word clouds.
