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Machine learning for emotion analysis in Python : build AI-powered tools for analyzing emotion using natural language processing and machine learning
Author
Title
Machine learning for emotion analysis in Python : build AI-powered tools for analyzing emotion using natural language processing and machine learning / Allan Ramsay, Tariq Ahmad.
ISBN
9781803240688
1803246715
9781803246710
Edition
1st edition.
Publication
Birmingham, UK : Packt Publishing Ltd., 2023.
Physical Description
1 online resource (334 pages) : illustrations
Local Notes
Access is available to the Yale community.
Notes
Includes index.
Access and use
Access restricted by licensing agreement.
Summary
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.
Variant and related titles
O'Reilly Safari. OCLC KB.
Format
Books / Online
Language
English
Added to Catalog
October 31, 2023
Contents
Cover
Title Page
Copyright and Credits
Contributors
Table of Contents
Preface
Part 1: Essentials
Chapter 1: Foundations
Emotions
Categorical
Dimensional
Sentiment
Why emotion analysis is important
Introduction to NLP
Phrase structure grammar versus dependency grammar
Rule-based parsers versus data-driven parsers
Semantics (the study of meaning)
Introduction to machine learning
Technical requirements
A sample project
Logistic regression
Support vector machines (SVMs)
K-nearest neighbors (k-NN)
Decision trees
Random forest
Neural networks
Making predictions
A sample text classification problem
Summary
References
Part 2: Building and Using a Dataset
Chapter 2: Building and Using a Dataset
Ready-made data sources
Creating your own dataset
Data from PDF files
Data from web scraping
Data from RSS feeds
Data from APIs
Other data sources
Transforming data
Non-English datasets
Evaluation
Summary
References
Chapter 3: Labeling Data
Why labeling must be high quality
The labeling process
Best practices
Labeling the data
Gold tweets
The competency task
The annotation task
Buy or build?
Results
Inter-annotator reliability
Calculating Krippendorff's alpha
Debrief
Summary
References
Chapter 4: Preprocessing
Stemming, Tagging, and Parsing
Readers
Word parts and compound words
Tokenizing, morphology, and stemming
Spelling changes
Multiple and contextual affixes
Compound words
Tagging and parsing
Summary
References
Part 3: Approaches
Chapter 5: Sentiment Lexicons and Vector-Space Models
Datasets and metrics
Sentiment lexicons
Extracting a sentiment lexicon from a corpus
Similarity measures and vector-space models
Vector spaces
Calculating similarity
Latent semantic analysis
Summary
References
Chapter 6: Naïve Bayes
Preparing the data for sklearn
Naïve Bayes as a machine learning algorithm
Naively applying Bayes' theorem as a classifier
Multi-label datasets
Summary
References
Chapter 7: Support Vector Machines
A geometric introduction to SVMs
Using SVMs for sentiment mining
Applying our SVMs
Using a standard SVM with a threshold
Making multiple SVMs
Summary
References
Chapter 8: Neural Networks and Deep Neural Networks
Single-layer neural networks
Multi-layer neural networks
Summary
References
Chapter 9: Exploring Transformers
Introduction to transformers
How data flows through the transformer model
Input embeddings
Positional encoding
Encoders
Decoders
Linear layer
Softmax layer
Output probabilities
Hugging Face
Existing models
Transformers for classification
Implementing transformers
Google Colab
Single-emotion datasets
Multi-emotion datasets
Summary
References
Genre/Form
Electronic books.
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