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

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.

Respective authors own all copyrights not held by the publisher.

The following publication is reproduced below with the goal of providing information specifically as accurate and reliable as possible. Regardless, purchasing this publication can be seen as consent to the fact that both the publisher and the author of this book are in no way experts on the topics discussed within and that any recommendations or suggestions that are made herein are for informational purposes only. Professionals should be consulted as needed prior to undertaking any of the action endorsed herein.

This declaration is deemed fair and valid by both the American Bar Association and the Committee of Publishers Association and is legally binding throughout the United States.

Furthermore, the transmission, duplication, or reproduction of any of the following work including specific information will be considered an illegal act irrespective of if it is done electronically or in print. This extends to creating a secondary or tertiary copy of the work or a recorded copy and is only allowed with express written consent from the Publisher. All additional rights reserved.

The information in the following pages is broadly considered to be a truthful and accurate account of facts and as such any inattention, use or misuse of the information in question by the reader will render any resulting actions solely under their purview. There are no scenarios in which the publisher or the original author of this work can be in any fashion deemed liable for any hardship or damages that may befall them after undertaking information described herein.

Additionally, the information in the following pages is intended only for informational purposes and should thus be thought of as universal. As befitting its nature, it is presented without assurance regarding its prolonged validity or interim quality. Trademarks that are mentioned are done without written consent and can in no way be considered an endorsement from the trademark holder.

The trademarks that are used are without any consent, and the publication of the trademark is without permission or backing by the trademark owner. All trademarks and brands within this book are for clarifying purposes only and are owned by the owners themselves, not affiliated with this document.

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