From the course: Build Three Real-World Python Applications

What is text analysis? - Python Tutorial

From the course: Build Three Real-World Python Applications

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What is text analysis?

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- In the previous chapter, we got a glimpse of some text analysis techniques with using the new sentiment Alpha Vantage function. In this chapter, we will explore some techniques for text analysis along with what they can do. Text analysis is where we use computers to analyze text data to better understand what is being said and the meaning behind it. This is part of what we call natural language processing. Natural language processing, also known as NLP, is a subfield of artificial intelligence where computers process and understand human language. The input may be written or spoken words for the computer to process. We have a wide range of what we can analyze with text analysis. For example, we can see what words are popular and show up frequently in our text. We can see what the overall sentiment is of the text such as if it is generally positive or negative. We can also discover what some common topics are in our text. Our analysis in Python will heavily use the NLTK Python package. The NLTK package stands for Natural Language Toolkit, and it is an industry standard to perform natural language processing. It has over 50 corporate and lexical resources along with libraries for tokenization, stemming, parsing, tagging, classification, and semantic reasoning. Why would we want to analyze text data in the first place? Text data is widely used throughout the internet and our daily lives. The more we can leverage these analyses, the more we can better understand human behavior, provide language-based solutions, and learn more about text products such as novels and blogs. Some applications of natural language processing are translators between two languages, a spell checker on a text application, a virtual assistant device on your computer, and a filter for filtering out spam emails. We will explore how to better understand the text from a novel in this chapter. Know that the techniques we teach can be expanded upon and use with other text data, such as analyzing product reviews, blog posts, news articles and social media posts.

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