Let's now apply these feature transformers and feature extractors to a very modern real-world use case—sentiment analysis. In sentiment analysis, the goal is to classify the underlying human sentiment—for example, whether the writer is positive, neutral, or negative towards the subject of a text. To many organizations, sentiment analysis is an important technique that is used to better understand their customers and target markets. For example, sentiment analysis can be used by retailers to gauge the public's reaction to a particular product, or by politicians to assess public mood towards a policy or news item. In our case study, we will examine tweets about airlines in order to predict whether customers are saying positive or negative things about them. Our analysis could then be used by airlines in order to improve...
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Machine Learning with Apache Spark Quick Start Guide
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Machine Learning with Apache Spark Quick Start Guide
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Overview of this book
Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently.
But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it?
The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data.
Table of Contents (10 chapters)
Preface
Free Chapter
The Big Data Ecosystem
Setting Up a Local Development Environment
Artificial Intelligence and Machine Learning
Supervised Learning Using Apache Spark
Unsupervised Learning Using Apache Spark
Natural Language Processing Using Apache Spark
Deep Learning Using Apache Spark
Real-Time Machine Learning Using Apache Spark
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