Machine Learning for Emotion Analysis in Python (eBook)
334 Seiten
Packt Publishing (Verlag)
978-1-80324-671-0 (ISBN)
Artificial intelligence and machine learning are the technologies of the future, and this is the perfect time to tap into their potential and add value to your business. Machine Learning for Emotion Analysis in Python helps you employ these cutting-edge technologies in your customer feedback system and in turn grow your business exponentially.
With this book, you'll take your foundational data science skills and grow them in the exciting realm of emotion analysis. By following a practical approach, you'll turn customer feedback into meaningful insights assisting you in making smart and data-driven business decisions.
The book will help you understand how to preprocess data, build a serviceable dataset, and ensure top-notch data quality. Once you're set up for success, you'll explore complex ML techniques, uncovering the concepts of deep neural networks, support vector machines, conditional probabilities, and more. Finally, you'll acquire practical knowledge using in-depth use cases showing how the experimental results can be transformed into real-life examples and how emotion mining can help track short- and long-term changes in public opinion.
By the end of this book, you'll be well-equipped to use emotion mining and analysis to drive business decisions.
Kickstart your emotion analysis journey with this step-by-step guide to data science successKey FeaturesDiscover the inner workings of the end-to-end emotional analysis workflowExplore the use of various ML models to derive meaningful insights from dataHone your craft by building and tweaking complex emotion analysis models with practical projectsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionArtificial intelligence and machine learning are the technologies of the future, and this is the perfect time to tap into their potential and add value to your business. Machine Learning for Emotion Analysis in Python helps you employ these cutting-edge technologies in your customer feedback system and in turn grow your business exponentially. With this book, you'll take your foundational data science skills and grow them in the exciting realm of emotion analysis. By following a practical approach, you'll turn customer feedback into meaningful insights assisting you in making smart and data-driven business decisions. The book will help you understand how to preprocess data, build a serviceable dataset, and ensure top-notch data quality. Once you're set up for success, you'll explore complex ML techniques, uncovering the concepts of deep neural networks, support vector machines, conditional probabilities, and more. Finally, you'll acquire practical knowledge using in-depth use cases showing how the experimental results can be transformed into real-life examples and how emotion mining can help track short- and long-term changes in public opinion. By the end of this book, you'll be well-equipped to use emotion mining and analysis to drive business decisions.What you will learnDistinguish between sentiment analysis and emotion analysisMaster data preprocessing and ensure high-quality inputExpand the use of data sources through data transformationDesign models that employ cutting-edge deep learning techniquesDiscover how to tune your models' hyperparametersExplore the use of naive Bayes, SVMs, DNNs, and transformers for advanced use casesPractice your newly acquired skills by working on real-world scenariosWho this book is forThis book is for data scientists and Python developers looking to gain insights into the customer feedback for their product, company, brand, governorship, and more. Basic knowledge of machine learning and Python programming is a must.]]>
| Erscheint lt. Verlag | 28.9.2023 |
|---|---|
| Sprache | englisch |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| ISBN-10 | 1-80324-671-5 / 1803246715 |
| ISBN-13 | 978-1-80324-671-0 / 9781803246710 |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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