IBM Watson Projects

Eight exciting projects that put artificial intelligence into practice for optimal business performance

James Miller

• Publisher: Packt Publishing
• Year: 2018
• Print ISBN: 9781789343717
• eBook ISBN: 9781789346695
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Incorporate intelligence to your data-driven business insights and high accuracy business solutions

Key Features

• Explore IBM Watson capabilities such as Natural Language Processing (NLP) and machine learning
• Build projects to adopt IBM Watson across retail, banking, and healthcare
• Learn forecasting, anomaly detection, and pattern recognition with ML techniques

Book Description

IBM Watson provides fast, intelligent insight in ways that the human brain simply can't match. Through eight varied projects, this book will help you explore the computing and analytical capabilities of IBM Watson.

The book begins by refreshing your knowledge of IBM Watson's basic data preparation capabilities, such as adding and exploring data to prepare it for being applied to models. The projects covered in this book can be developed for different industries, including banking, healthcare, media, and security. These projects will enable you to develop an AI mindset and guide you in developing smart data-driven projects, including automating supply chains, analyzing sentiment in social media datasets, and developing personalized recommendations.

By the end of this book, you'll have learned how to develop solutions for process automation, and you'll be able to make better data-driven decisions to deliver an excellent customer experience.

What you will learn

• Build a smart dialog system with cognitive assistance solutions
• Design a text categorization model and perform sentiment analysis on social media datasets
• Develop a pattern recognition application and identify data irregularities smartly
• Analyze trip logs from a driving services company to determine profit
• Provide insights into an organization's supply chain data and processes
• Create personalized recommendations for retail chains and outlets
• Test forecasting effectiveness for better sales prediction strategies

Who this book is for

This book is for data scientists, AI engineers, NLP engineers, machine learning engineers, and data analysts who wish to build next-generation analytics applications. Basic familiarity with cognitive computing and sound knowledge of any programming language is all you need to understand the projects covered in this book.

Social Media Sentiment Analysis

In this chapter, we will learn about sentiment analysis: using Watson Analytics to automatically analyze and categorize text posted to social media, in an attempt to determine an audience's feelings about a topic.

As with our previous chapters, the breakdown for this chapter will be as follows:

• The problem defined
• Getting started
• Building the project
• Reviewing the results

The problem defined

Sentiment analysis, or mining for opinions – when we perform this type of project, multiple technologies can be leveraged, for example Natural Language Processing (NLP ), textual analysis, and computational linguistics, to identify and extract subjective material from a source. Sentiment analysis is and has been used in a variety of ways, and more opportunities appear every day. One interesting example is that it is used by political candidates and administrations in an attempt to monitor overall opinions about policy changes and campaign announcements, empowering them to fine-tune their approach and messaging to better relate to voters and constituents.

To further drive the point, in a recent blog posting, Mike Waldron, Head of Marketing and Sales at AYLIEN , said the following:

"As more and more content is created and shared online, through social channels, blogs, review sites and so on, we are becoming more and more vocal and open about our experiences online. In a recent study carried out by Zendesk, it was noted that 45% of people share bad customer service experiences and 30% share good customer service experiences via social media (https://www.zendesk.com/resources/customer-service-and-lifetime-customer-value/)", which again highlights" the need and desire for businesses to mine this information to gain business insight from it has also increased."

Social media and IBM Watson Analytics

IBM Watson Analytics for Social Media is a fairly new offering in Watson Analytics and is designed to help you analyze content in social media posts, even those that are in Arabic, French, German, Italian, Portuguese, or Spanish.

As with all of Watson Analytics, Watson Analytics for Social Media aims to discover insights; this feature mines conversations in social media that are found to be related to your interest, with the intent of answering questions such as the following (listed in the product documentation):

• What are customers hearing and saying about my brand?
• What are the most talked about product qualities in my product grouping?
• Are reactions respectable, good, or not so much?
• What is the opposition doing for market stimulation?
• Is my employer's reputation affecting my capability to recruit the highest talent?
• What are the reputations of the new sellers that I may be considering?
• What issues are most significant for my community?

To become familiar with this feature, in this chapter we will create a Watson Analytics project in an attempt to analyze and understand a topic of interest by specifying certain topics and keywords, as well as by using various pertinent Watson Analytics-produced ideas around keywords.

Let's get started!

Getting started

There is a process to follow to use Watson Analytics for Social Media. The process is referred to as the social media workflow. It is similar to the process (or workflow) that should be followed for any Watson Analytics project. The workflow is as follows:

1. Create a Watson Analytics project
2. Identify or create a data source (or dataset)
3. View the Watson Analytics-generated visualizations

Creating a Watson Analytics social media project

For social media projects, the particulars of this workflow process look like this:

• Specify topics to analyze: You specify the keywords to include, plus you can also exclude keywords and define context keywords. Use suggestions that we provide to get more ideas about keywords.
• Specify the date range: By default, the last four weeks are searched and analyzed.
• Specify the types of sources that you want to retrieve documents from: Sources that are available include forums, reviews, Facebook pages, Reddit pages, video descriptions and comments, blogs, and news.
• Review the suggestions for your topics, because new suggestions might appear after you specify the date range and sources: Optionally, define themes to provide more clarity in the analysis.
• Identify or create a dataset...

Table of contents

• Title Page
• Copyright and Credits
• Packt Upsell
• Contributors
• Preface
• The Essentials of IBM Watson
• A Basic Watson Project
• An Automated Supply Chain Scenario
• Healthcare Dialoguing
• Social Media Sentiment Analysis
• Pattern Recognition and Classification
• Retail and Personalized Recommendations
• Integration for Sales Forecasting
• Anomaly Detection in Banking Using AI
• What's Next
• Other Books You May Enjoy