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Dipanjan Sarkar - Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from Your Data

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Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from Your Data
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Dipanjan Sarkar
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Apress
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2016
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Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem.

Text Analytics with Python teaches you both basic and advanced concepts, including text and language syntax, structure, semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization.

A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a birds eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems.

This book:

Provides complete coverage of the major concepts and techniques of natural language processing (NLP) and text analytics
Includes practical real-world examples of techniques for implementation, such as building a text classification system to categorize news articles, analyzing app or game reviews using topic modeling and text summarization, and clustering popular movie synopses and analyzing the sentiment of movie reviews
Shows implementations based on Python and several popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern

What you will learn:

Natural Language concepts

Analyzing Text syntax and structure

Text Classification

Text Clustering and Similarity analysis

Text Summarization

Semantic and Sentiment analysis

Readership :

The book is for IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data.

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Dipanjan Sarkar 2016

Dipanjan Sarkar Text Analytics with Python 10.1007/978-1-4842-2388-8_1

1. Natural Language Basics

Dipanjan Sarkar 1

(1)

Bangalore, Karnataka, India

Electronic supplementary material

The online version of this chapter (doi: 10.1007/978-1-4842-2388-8_1 ) contains supplementary material, which is available to authorized users.

We have ushered in the age of Big Data where organizations and businesses are having difficulty managing all the data generated by various systems, processes, and transactions. However, the term Big Data is misused a lot due to the nature of its popular but vague definition of the 3 Vsvolume, variety, and velocity of data. This is because sometimes it is very difficult to exactly quantify what data is Big. Some might think a billion records in a database would be Big Data, but that number seems really minute compared to the petabytes of data being generated by various sensors or even social media. There is a large volume of unstructured textual data present across all organizations, irrespective of their domain. Just to take some examples, we have vast amounts of data in the form of tweets, status updates, comments, hashtags, articles, blogs, wikis, and much more on social media. Even retail and e-commerce stores generate a lot of textual data from new product information and metadata with customer reviews and feedback.

The main challenges associated with textual data are twofold. The first challenge deals with effective storage and management of this data. Usually textual data is unstructured and does not adhere to any specific predefined data model or schema, which is usually followed by relational databases. However, based on the data semantics, you can store it in either SQL-based database management systems (DBMS ) like SQL Server or even NoSQL-based systems like MongoDB. Organizations having enormous amounts of textual datasets often resort to file-based systems like Hadoop where they dump all the data in the Hadoop Distributed File System (HDFS) and access it as needed, which is one of the main principles of a data lake .

The second challenge is with regard to analyzing this data and trying to extract meaningful patterns and useful insights that would be beneficial to the organization. Even though we have a large number of machine learning and data analysis techniques at our disposal, most of them are tuned to work with numerical data, hence we have to resort to areas like natural language processing (NLP ) and specialized techniques, transformations, and algorithms to analyze text data, or more specifically natural language , which is quite different from programming languages that are easily understood by machines. Remember that textual data, being highly unstructured, does not follow or adhere to structured or regular syntax and patternshence we cannot directly use mathematical or statistical models to analyze it.

Before we dive into specific techniques and algorithms to analyze textual data, we will be going over some of the main concepts and theoretical principles associated with the nature of text data in this chapter. The primary intent here is to get you familiarized with concepts and domains associated with natural language understanding , processing , and text analytics . We will be using the Python programming language in this book primarily for accessing and analyzing text data. The examples in this chapter will be pretty straightforward and fairly easy to follow. However, you can quickly skim over Chapter which includes programs, code snippets and datasets. This chapter covers concepts relevant to natural language, linguistics, text data formats, syntax, semantics, and grammars before moving on to more advanced topics like text corpora , NLP, and text analytics.

Natural Language

Textual data is unstructured data but it usually belongs to a specific language following specific syntax and semantics. Any piece of text dataa simple word, sentence, or documentrelates back to some natural language most of the time. In this section, we will be looking at the definition of natural language, the philosophy of language, language acquisition, and the usage of language.

What Is Natural Language?

To understand text analytics and natural language processing , we need to understand what makes a language natural. In simple terms, a natural language is one developed and evolved by humans through natural use and communication , rather than constructed and created artificially, like a computer programming language.

Human languages like English, Japanese, and Sanskrit are natural languages. Natural languages can be communicated in different forms, including speech, writing, or even signs. There has been a lot of scholarship and effort applied toward understanding the origins, nature, and philosophy of language. We will discuss that briefly in the following section.

The Philosophy of Language

We now know what a natural language means. But think about the following questions. What are the origins of a language ? What makes the English language English? How did the meaning of the word fruit come into existence? How do humans communicate among themselves with language? These are definitely some heavy philosophical questions.

The philosophy of language mainly deals with the following four problems and seeks answers to solve them:

The nature of meaning in a language
The use of language
Language cognition
The relationship between language and reality
The nature of meaning in a language is concerned with the semantics of a language and the nature of meaning itself. Here, philosophers of language or linguistics try to find out what it means to actually mean anythingthat is, how the meaning of any word or sentence originated and came into being and how different words in a language can be synonyms of each other and form relations. Another thing of importance here is how structure and syntax in the language pave the way for semantics, or to be more specific, how words, which have their own meanings, are structured together to form meaningful sentences. Linguistics is the scientific study of language, a special field that deals with some of these problems we will be looking at in more detail later on. Syntax, semantics, grammars, and parse trees are some ways to solve these problems. The nature of meaning can be expressed in linguistics between two human beings, notably a sender and a receiver, as what the sender tries to express or communicate when they send a message to a receiver, and what the receiver ends up understanding or deducing from the context of the received message. Also from a non-linguistic standpoint, things like body language, prior experiences, and psychological effects are contributors to meaning of language, where each human being perceives or infers meaning in their own way, taking into account some of these factors.
The use of language is more concerned with how language is used as an entity in various scenarios and communication between human beings. This includes analyzing speech and the usage of language when speaking, including the speakers intent, tone, content and actions involved in expressing a message. This is often termed as a speech act in linguistics. More advanced concepts such as the origins of language creation and human cognitive activities such as language acquisition which is responsible for learning and usage of languages are also of prime interest.
Language cognition specifically focuses on how the cognitive functions of the human brain are responsible for understanding and interpreting language. Considering the example of a typical sender and receiver, there are many actions involved from message communication to interpretation. Cognition tries to find out how the mind works in combining and relating specific words into sentences and then into a meaningful message and what is the relation of language to the thought process of the sender and receiver when they use the language to communicate messages.

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