NLP Techniques in Data Science - Real-Life Applications

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NLP Techniques in Data Science -  Real-Life Applications

A well-known area of AI called Natural Language Processing (NLP) aids Data Science in gleaning information from textual data. Here we will learn how NLP works.

Today, an alarming amount of data is being generated. The text makes up a sizable amount of the already available data. A well-known area of AI called Natural Language Processing (NLP) aids Data Science in gleaning information from textual data.

Following this, industry insiders have forecast that there will soon be a great need for expertise in natural language processing.

Natural Language Processing

The field of study known as "natural language processing," or NLP, aims to teach computers how to read and comprehend text like humans. It is a field-creating approach to bridging the divide between human languages and data science.

Everything we say or write has essential information and may be used to help us make wise decisions. However, since people utilize a variety of languages, words, tones, etc., obtaining this information is not that simple. Our discussions, tweets, and other data-generating activities produce a tonne of highly unstructured data. Traditional methods are unable to conclude from this data.

NLP is being used in many fields, including healthcare, finance, media, and human resources, to make use of text and speech data that is accessible. NLP is used to create a large number of text and speech recognition applications. Siri, Cortana, Alexa, and other personal speech assistants are a few examples.

Bag of Words

This model determines the total number of words in a document. The way this approach operates is by creating a matrix of sentence occurrences. When creating the matrix, neither the underlying grammar nor the word order is taken into account. These occurrences or numbers are subsequently used as features by the classifier.

The result of a coordinating conjunction connecting two or more main clauses is a compound sentence. A statement with only one major clause is referred to as simple.

Term Frequency-Inverse Document Frequency (TF-IDF)

By employing a weighting factor, Term Frequency-Inverse Document Frequency, or TF-IDF, gets beyond the limitations of Bag of Words. It determines the significance of a word in a document using statistics. Let's examine the TF-IDF numbers.

Term frequency, often known as TF, is a measure of how often a term appears in a document. To determine this, multiply the total number of times the word appears in the text by the length of the entire document.

Tokenization in NLP

The entire text is divided into sentences and words using one of the NLP approaches. In other words, it is a procedure for breaking the text into units called tokens. Certain characters, including hyphens and punctuation, are removed during this procedure. Tokenization's primary goal is to transform the text into a format that makes analysis easier.

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Stop Words Removal

Tokenization and Stop Words Removal both have similar goals. The most popular terms that are used frequently but offer nothing to the final product are eliminated throughout this procedure. Consider the frequent prepositions used in English, such as and, the, a, etc. The fundamental goal of this is to reduce background noise so that we can concentrate during analysis on the words that have significant significance.

By searching for the words you want to eliminate in a list that has already been created, you may easily remove stop words. In addition to improving performance and processing speed, this helps save up space.

Stemming in NLP

Reducing words to their base form is the major goal of this NLP method. This method is based on the idea that words with roughly identical meanings but slightly different spellings should be in the same token. To ensure quick processing, we remove the word affixes.

Lemmatization in NLP

Similar to stemming, lemmatization has this objective. This NLP approach seeks to group together and deduce the root form of a word from its inflected forms. For instance,

Worst is changed to bad, and came is changed from came (past tense to present tense) (synonyms to their simpler form)

Lemmatization and stemming have a similar end goal, but they employ distinct methods to get there.

In lemmatization, the words are changed into a lemma, which is the word's dictionary form. For instance, the words "swim" might also be spelled "swims" or "swimming." Therefore, the lemma for these words will be "swim."

Topic Modeling in NLP

The NLP approach known as "Topic Modeling" pulls the major subjects from the text or document. It operates on the premise that each document is composed of a collection of subjects, and each topic is composed of a group of words. It's comparable to Dimensionality Reduction. Because we also break down the lengthy material using this strategy into a more manageable amount of sections.

Numerous data science applications use the topic modeling approach, including data analysis, classification, recommender systems, etc.

Word Embeddings in NLP

It is a method for numerically expressing the words in a text. Its depiction needs to be so that words with comparable meanings are represented similarly. The words are defined as real-valued vectors in contemporary techniques.

Although each word vector has a different value, they all have the same length. The difference between the two vectors shows how similar they are.

Conclusion

We have witnessed the employment of several well-known NLP approaches in data science. We have also observed how many businesses use data science and natural language processing to enhance their operations. When employing NLP in data science, these NLP techniques are particularly beneficial. Anyone can master data science with the best data science course in Bangalore, which provides you with domain-specialized training along with capstone projects.