Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning

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Publisher ‏ : ‎ O’Reilly Media; 1st edition (July 31, 2018)
Language ‏ : ‎ English
Paperback ‏ : ‎ 330 pages
ISBN-10 ‏ : ‎ 1491963042
ISBN-13 ‏ : ‎ 978-1491963043
Item Weight ‏ : ‎ 1.22 pounds
Dimensions ‏ : ‎ 7.25 x 0.75 x 9.25 inches

In today’s digital age, text data is increasingly becoming a valuable source of information for businesses. From customer reviews and social media posts to emails and news articles, the amount of text data generated every day is staggering. To make sense of this vast amount of unstructured text data, businesses are turning to text analysis techniques powered by machine learning.

One popular programming language for text analysis is Python, which offers a wide range of libraries and tools for natural language processing (NLP) and machine learning. With Python, businesses can build language-aware data products that can extract insights, sentiment, and trends from text data.

In this post, we will explore how Python can be used for applied text analysis to enable language-aware data products with machine learning. We will cover key concepts and techniques such as tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, topic modeling, and more.

By the end of this post, you will have a solid understanding of how Python can be leveraged to unlock the value of text data and build data products that can revolutionize the way businesses interact with their customers and make informed decisions.

So, whether you are a data scientist, a business analyst, or a developer, join us on this journey to discover the power of applied text analysis with Python. Let’s unlock the secrets hidden in text data and create language-aware data products that drive business success.
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