Text Analytics with Python: A Brief Introduction to Text Analytics with Python

Text analytics is all about obtaining relevant and useful information from some unstructured data. Text analytics techniques can be of great importance and can provide amazing helo for various organizations that aim to derive some potentially valuable business insights from an amazingly large collection of text-based content like social media streams, emails or word documents. Sure, text analytics using natural language processing, machine learning, and statistical modeling can be very challenging since human language is commonly inconsistent. It contains various ambiguities mainly caused by inconsistent semantics and syntax. Fortunately, text analytics software can easily help you by transposing phrases and words contained in unstructured data into some numerical values that you later link with structured data contained in data set. It is more than apparent that major enterprises are increasingly and rapidly turning to text analytics techniques in order to improve their businesses as well as overall customer satisfaction. We are witnesses that amazing variety and volume when it comes to data generated across different feedback channels continues to grow and expand providing various businesses with a wealth of valuable information regarding their customers. It is more than apparent that sifting through all available content would be amazingly time-consuming to be done manually.However, understanding those insights held in data is more than critical when it comes to the getting an accurate view of customers' voice. We are also witnessing the next chapter of text analytics approach since it already developing that solid ground. It will also continue to be among other technical necessities today and into the future. In order to keep up with the future, embark on your own text analytics journey having this book by your side as your best companion.

What you will learn by reading this book:

• Text analytics process
• How to build a corpus and analyze sentiment
• Named entity extraction with Groningen meaning bank corpus
• How to train your system
• Getting started with NLTK
• How to search syntax and tokenize sentences
• Automatic text summarization
• Stemming word and topic modeling with NLTK
• Using scikit-learn for text classification
• Part of speech tagging and POS tagging models in NLTK
• And much, much more...

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