| Description |
1 online resource (334 pages) : illustrations |
| Note |
Includes index. |
| 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. |
| 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 |
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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 |
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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 |
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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 |
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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 |
| Subject |
Machine learning.
|
|
Emotion recognition.
|
|
Apprentissage automatique. |
|
Reconnaissance des émotions. |
|
Emotion recognition |
|
Machine learning |
| Added Author |
Ahmad, Tariq, author.
|
| ISBN |
1803246715 (electronic bk.) |
|
9781803246710 (electronic bk.) |
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