Sentiment analysis for chatbots DialogFlow, IBM Watson

Sentiment analysis for chatbots – DialogFlow, IBM Watson, available at $19.99, has an average rating of 4.35, with 35 lectures, based on 28 reviews, and has 347 subscribers.

You will learn about How sentiment analysis can benefit a chatbot Common implementations scenarios for a chatbot Use DialogFlows sentiment analysis to make chatbot emotionally sensitive. Youll have a chatbot that can read the sentiment of the users message and act on it Learn how to use IBM Watson for sentiment analysis Learn how to use AFINN sentiment analysis This course is ideal for individuals who are chatbot owners that want to improve chatbot performance It is particularly useful for chatbot owners that want to improve chatbot performance.

Enroll now: Sentiment analysis for chatbots – DialogFlow, IBM Watson

Summary

Title: Sentiment analysis for chatbots – DialogFlow, IBM Watson

Price: $19.99

Average Rating: 4.35

Number of Lectures: 35

Number of Published Lectures: 34

Number of Curriculum Items: 35

Number of Published Curriculum Objects: 34

Original Price: 114.99

Quality Status: approved

Status: Live

What You Will Learn

How sentiment analysis can benefit a chatbot

Common implementations scenarios for a chatbot

Use DialogFlows sentiment analysis to make chatbot emotionally sensitive. Youll have a chatbot that can read the sentiment of the users message and act on it

Learn how to use IBM Watson for sentiment analysis

Learn how to use AFINN sentiment analysis

Who Should Attend

chatbot owners that want to improve chatbot performance

Target Audiences

chatbot owners that want to improve chatbot performance

In this course,you’ll learn how to implement a sentiment analysis to a chatbot. You will teach your bot how to be emotionally sensitive. And most urgently how to spot a frustrated user and save the day.

In the course, we’ll be working with my code from the messenger chatbot course. Even if you did not take that course, you’d be able to take the code and work with it.

We’ll use Node.js for programming and GIT for deploying and version control. The bot will is hosted on Heroku, but you can simply host it anywhere else where they support Node.js. We’ll use DialogFlow to process natural language.DialogFlow will help us understand what users want.

In the process, you’ll learn how sentiment analysis works behind the scenes and how to implement sentiment analysis to a chatbot.

In the first section, we’ll take an overview of the app infrastructure and get familiar with the tech stack, which is the technology used in this course.

The second section will introduce you to Sentiment analysis, what is it and how it can benefit a chatbot. We’ll look at common implementation scenarios in a chatbot.

Do you want to know the difference between the rule-based approach and automatic approach with machine learning? We’ll go through examples of both and make a little demo for each. You’ll get familiar with dictionary-based solution AFFIN and Google’s natural language API and IBM Watson natural language solution. I will gently introduce you to the algorithms they use and how they work. So you know how things work inside that black box.

Then in the third section, we start implementing a dictionary-based sentiment analysis. We’ll use my chatbot as a sample. First, I’ll give you an overview of the code and show you where and how to get a sentiment result for the user message. Then we’ll create a sentiment analysis module and teach a bot how to route to live agent upon identifying a super frustrated user. We’ll also teach a chatbot to be sensitive to sentiment change and intervene when it detects user’s experience satisfaction is decreasing. And then I’ll give you another candy. I’ll show you how to send a notification to Slack when a frustrated user comes along.

When you know how to implement a dictionary-based solution, we’ll move to a machine learning solution. And that is what we’ll do in section 4. One of the best is Google’s natural language solution. We will do the full implementation into a chatbot.

And in this demo, we’ll use a chatbot from my previous course. You’ll get the source code and will be able to work with it even if you did not take that course.

After the course, you’ll have a demo bot with built-in sentiment analysis and knowledge of how to implement it into your chatbot.

To make it easier for you I’ve added git commits with changes to every video that has a change in the code. This way, you can compare your code to mine. And you won’t lose time debugging. And remember for any questions I’m available in the Questions and answers. You are not alone.

And remember, I’LL BE THERE FOR YOU.

My name is Jana, and I’ll be your instructor in this course. I’m a web engineer with 20 years of programming experience. I’m also an IT instructor teaching people new tech skills. Over 17000 people are already taking my course.

I help all my students at every step of development. And I’ll be here for you!

At the end of the course, you’ll have a chatbot that can read the sentiment of the user’s message and act on it. Wait no longer. Take the course and make your chatbot even better.

See you in the course!

Jana

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Tech-stack

Lecture 3: Behind the scenes – bot architecture

Lecture 4: How to get help

Lecture 5: Additional resources

Chapter 2: About Sentiment analysis

Lecture 1: Sentiment analysis in a nutshell

Lecture 2: Sentiment analysis for a chatbot

Lecture 3: Common implementation scenarios in chatbot

Lecture 4: Sentiment analysis examples – AFINN sentiment lexicon

Lecture 5: Sentiment analysis examples – Google NLU

Lecture 6: Sentiment analysis examples – IBM watson

Lecture 7: How does sentiment analysis work? – Part 1

Lecture 8: How does sentiment analysis work? – Part 2

Lecture 9: How does sentiment analysis work? – Part 3

Lecture 10: Sentiment Analysis tools & APIs

Chapter 3: Implementation for a chatbot – 1 example

Lecture 1: My bot with AFINN sentiment analysis

Lecture 2: Me bot code overview

Lecture 3: Get sentiment result of user message

Lecture 4: Create sentiment service module

Lecture 5: Route to LIVE agent when sentiment is too negative

Lecture 6: Intervene when sentiment changes

Lecture 7: Slack webhook verification

Lecture 8: Send notification to slack when live agent needed

Chapter 4: Implementation for a chatbot – 2 example

Lecture 1: How to get Smartbabe working – FB page, FB app

Lecture 2: How to get Smartbabe working – Dialogflow

Lecture 3: How to get Smartbabe working – backend app, webhook validation

Lecture 4: Code overview

Lecture 5: Enable sentiment analysis in DialogFlow

Lecture 6: Check sentiment analysis results

Lecture 7: Call live agent on low sentiment

Lecture 8: Intervene on sentiment change

Lecture 9: Sentiment analysis challenges today

Chapter 5: Platforms, NLP & Libraries for Voice Bots &?Chatbots

Lecture 1: Tools for chatbot and voice bot developers

Chapter 6: Conclusion

Lecture 1: Conclusion

Instructors

Jana Bergant
Web developer, IT instructor

Rating Distribution

1 stars: 1 votes

2 stars: 1 votes

3 stars: 1 votes

4 stars: 7 votes

5 stars: 18 votes

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