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Machine Learning for Emotion Analysis in Python
Author(s): Allan Ramsay, and Tariq Ahmad
Publisher: Packt Publishing Pvt. Ltd., Year: 2023
Kickstart your emotion analysis journey with this hands-on, step-by-step guide to data science success
Key Features
Discover the ins and outs of the end-to-end emotional analysis workflow
Explore the use of various ML models to derive meaningful insights from all sorts of data
Hone your craft by building and tweaking complex emotion analysis models in practical projects
Book Description
The AI winter has long thawed, but many organizations are still failing to harness the power of machine learning (ML). If you want to tap that potential and add value to your own business with cutting-edge emotion analysis, you’ve found what you need in this trusty guide.
In Machine Learning for Emotion Analysis, you’ll take your foundational data science skills and grow them in the exciting realm of emotion analysis. With its practical approach, you’ll be equipped with everything you need to give your company a clear insight into what your customers are thinking.
This no-nonsense guide jumps right into the practicalities of emotion analysis, teaching you how to preprocess data, build a serviceable dataset, and ensure top-notch data quality. Once you’re set up for success, we get hands-on with complex ML techniques. This is where you go from the intermediate to the advanced, covering deep neural networks, support vector machines, conditional probabilities, and more, as you experience the full breadth of possibilities with emotion analysis. The book finally rounds out with a couple of in-depth use cases – a sort of sandbox for you to experiment with your newly acquired skill set.
By the end of this book, you’ll be ready to present yourself as a valuable asset to any organization that takes data science seriously.
What you will learn
Distinguish between sentiment analysis and emotion analysis
Master the art of data preprocessing and ensure high-quality input
Expand your use of data sources through data transformation
Build models that employ cutting-edge deep learning techniques
Discover how best to tune your models’ hyperparameters
Explore the use of KNN, SVM, and DNNs for advanced use cases
Build APIs and integrate your models into existing solutions
Practice your new skills by working on real-world scenarios
Key Features
Discover the ins and outs of the end-to-end emotional analysis workflow
Explore the use of various ML models to derive meaningful insights from all sorts of data
Hone your craft by building and tweaking complex emotion analysis models in practical projects
Book Description
The AI winter has long thawed, but many organizations are still failing to harness the power of machine learning (ML). If you want to tap that potential and add value to your own business with cutting-edge emotion analysis, you’ve found what you need in this trusty guide.
In Machine Learning for Emotion Analysis, you’ll take your foundational data science skills and grow them in the exciting realm of emotion analysis. With its practical approach, you’ll be equipped with everything you need to give your company a clear insight into what your customers are thinking.
This no-nonsense guide jumps right into the practicalities of emotion analysis, teaching you how to preprocess data, build a serviceable dataset, and ensure top-notch data quality. Once you’re set up for success, we get hands-on with complex ML techniques. This is where you go from the intermediate to the advanced, covering deep neural networks, support vector machines, conditional probabilities, and more, as you experience the full breadth of possibilities with emotion analysis. The book finally rounds out with a couple of in-depth use cases – a sort of sandbox for you to experiment with your newly acquired skill set.
By the end of this book, you’ll be ready to present yourself as a valuable asset to any organization that takes data science seriously.
What you will learn
Distinguish between sentiment analysis and emotion analysis
Master the art of data preprocessing and ensure high-quality input
Expand your use of data sources through data transformation
Build models that employ cutting-edge deep learning techniques
Discover how best to tune your models’ hyperparameters
Explore the use of KNN, SVM, and DNNs for advanced use cases
Build APIs and integrate your models into existing solutions
Practice your new skills by working on real-world scenarios
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