NLP Techniques Every Data Scientist Should Know

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Natural Language Processing (NLP) is a crucial area of Data Science that enables machines to understand and process human language. For aspiring data scientists, mastering NLP techniques can open doors to various exciting career opportunities. Here’s a comprehensive guide to the essential NLP techniques you should know.

1) Tokenization

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Description: Tokenization is the process of splitting text into individual words or sentences, known as tokens. It is the first step in text preprocessing.

Example: For the sentence “KSR Datavision offers top-notch data courses,” tokenization would produce [“KSR”, “Datavision”, “offers”, “top-notch”, “data”, “courses”].

Real-Time Use Case: Tokenization is used in search engines to index words and improve search accuracy.

2) Stop Words Removal

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Description: Stop words are common words like “is,” “and,” “the,” which are often removed from text as they add little value to the analysis.

Example: Removing stop words from “KSR Datavision offers the best courses” results in [“KSR”, “Datavision”, “offers”, “best”, “courses”].

Real-Time Use Case: Stop words removal is crucial in sentiment analysis to focus on meaningful words.

3) Stemming and Lemmatization

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Description: Both techniques reduce words to their base or root form. Stemming cuts off prefixes/suffixes, while lemmatization considers the context.

Example: The word “running” becomes “run” through stemming and “run” through lemmatization.

Real-Time Use Case: Used in text summarization to identify the main content.

4) Bag of Words (BoW)

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Description: BoW is a representation of text that describes the occurrence of words within a document. It ignores grammar and word order but keeps multiplicity.

Example: For “KSR Datavision offers data courses” and “data courses by KSR,” BoW representation is similar, highlighting word frequency.

Real-Time Use Case: Commonly used in document classification

5) Term Frequency-Inverse Document Frequency (TF-IDF)

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Description: TF-IDF is a statistical measure to evaluate the importance of a word in a document relative to a corpus.

Example: In a large corpus of data science articles, “data” might appear frequently, but “Datavision” might be more unique, giving it higher importance.

Real-Time Use Case: Used in information retrieval and search engines to rank documents.

6) Named Entity Recognition (NER)

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Description: NER identifies and classifies named entities in text into predefined categories like names of persons, organizations, locations, etc.

Example: In “KSR Datavision, located in India, offers courses,” NER identifies “KSR Datavision” as an organization and “India” as a location.

Real-Time Use Case: Used in news categorization and information extraction.

7) Sentiment Analysis

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Description: Sentiment analysis determines the sentiment expressed in text, such as positive, negative, or neutral.

Example: Analyzing “KSR Datavision offers excellent courses” would result in a positive sentiment.Real-Time Use Case: Used in social media monitoring to gauge public opinion.

Real-Time Use Case: Used in social media monitoring to gauge public opinion.

8) Word Embeddings

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Description: Word embeddings are dense vector representations of words that capture semantic relationships between them.

Example: In embeddings, “data” and “science” might have vectors close to each other, indicating their relatedness.

Real-Time Use Case: Used in machine translation and question-answering systems.

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