Text Analytics with Python: A Brief Introduction to Text Analytics with Python
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About this ebook
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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Text Analytics with Python - Anthony S. Williams
Text Analytics with Python
A Brief Introduction to Text Analytics with Python
By Anthony S. Williams
© Copyright 2017 by Anthony S. Williams - All rights reserved.
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Table of Contents
Introduction
Chapter 1 Text Analytics Process
Building a Corpus
Analyzing Sentiment
Visualizing Your Results
Chapter 2 Named Entity Extraction
Groningen Meaning Bank Corpus
Training Your System
Chapter 3 Getting Started with NLTK
Searching Syntax
Sentence Tokenization
Chapter 4 Automatic Text Summarization
Chapter 5 Text Classification Using Scikit-Learn and NLTK
Stemming Words with NLTK
Topic Modeling
Chapter 6 Part of Speech Tagging
POS Tagging Model in NLTK
Conclusion
Introduction
Text analytics, or text mining, is all about deriving some high-quality structured data from obtained unstructured data. A very good reason for using text analytics might be extracting some additional data about customers from obtained unstructured data sources, in order to enrich that customer master data and to produce entirely new customer insight as well as to determine sentiment about different services and products. The most common text analytics applications are in case of management, for instance, healthcare patient records, and insurance claims assessments. Text analytics is widely used as well in competitor analysis, media coverage analysis, sentiment analytics, voice of the customer, pharmaceutical drug trial improvement, fault management, and different fields of service optimization.
Text mining is the process of converting obtained unstructured data into some meaningful data notably used for further analysis, especially when it comes to measuring customer opinions, feedback, and product reviews to provide that search facility, entity modeling, and sentimental analysis in order to support mainly fact-based decision making. Text mining uses techniques from several different fields including machine learning techniques, statistical, and linguistic techniques. Text mining also involves different information particularly retrieved from unstructured data as well as the process of properly interpreting that output data in order to derive trends and patterns as well as to evaluate and interpret the output data.
Text mining commonly involves categorization, lexical analysis, pattern recognition, annotation, clustering, tagging, visualization, information extraction, predictive analytics, and association analysis. This certain field of data analytics provides different tools, algorithm-based applications, extraction tools, and servers used for converting unstructured data into some meaningful data notably used for further analysis. The outputs of such data analytics are some extracted entities and facts particularly with certain relationships stored in some relational XML or some other