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Basic NLP tasks with Python, End-to-end sentiment analysis, Fake news detection, personality prediction, Span Filtering, Text classification, Topic modeling, Twitter sentiment analysis, Yelp review, etc with NLP

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Basic understanding in NLP

What is NLP?

Natural Language Processing is an area of computer science concerned with interaction between computers and human languages. The primary aim is to enable a computer to process, understand, interpret and respond to human language in a meaningful way. This involves processing and analyzing large volumes of natural language data using computational techniques.

Applications of NLP in real life:

  • Text Classification: Categorizing emails into spam or non-spam.
  • Sentiment Analysis: Determining whether a customer review is positive, negative, or neutral.
  • Machine Translation: Automatically translating text from one language to another, e.g., translating any text from English to Spanish.
  • Speech Recognition: Converting spoken language into text, such as voice commands in virtual assistants like Siri or Alexa.
  • Named Entity Recognition (NER): Identifying and classifying proper names in text, such as names of people, organizations, or locations. Natural language processing is a fast-evolving field. There are plenty of libraries that can be used for NLP. In this article, we will focus on Spacy and NLTK for our NLP analysis.

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Basic NLP tasks with Python, End-to-end sentiment analysis, Fake news detection, personality prediction, Span Filtering, Text classification, Topic modeling, Twitter sentiment analysis, Yelp review, etc with NLP

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