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
Kickstart your emotion analysis journey with this step-by-step guide to data science success Key Features Discover the inner workings of the end-to-end emotional analysis workflow Explore the use of various ML models to derive meaningful insights from data Hone your craft by building and tweaking co...
| Otros Autores: | , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, United Kingdom :
Packt Publishing Ltd
2023.
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| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009769035406719 |
Tabla de Contenidos:
- 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
- Chapter 10: Multiclassifiers
- Multilabel datasets are hard to work with
- Confusion matrices
- Using "neutral" as a label
- Thresholds and local thresholds
- Multiple independent classifiers
- Summary
- Part 4: Case Study
- Chapter 11: Case Study - The Qatar Blockade
- The case study
- Short-term changes
- Long-term changes
- Proportionality revisited
- Summary.
