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Advanced Tokenization and Sentiment Analysis · LearnSpace
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Advanced Tokenization and Sentiment Analysis

Курс от Edureka
Средний≈ 16.2 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course offers a clear pathway to undertsand advanced tokenization and sentiment analysis—two core pillars of modern NLP. You'll learn how to convert raw text into structured input using subword, character-level, and adaptive tokenization techniques, and how to extract sentiment using rule-based, statistical, and deep learning models. Through hands-on exercises, you’ll gain the skills to handle complex language input, model sentiment at fine granularity, and deploy systems that generalize across domains and languages. By the end of this course, you will be able to: - Explain and apply advanced tokenization techniques, including BPE, character-level, and streaming methods - Handle out-of-vocabulary terms and domain-specific language using adaptive and hybrid encoding strategies - Build sentiment analysis models using VADER, Naïve Bayes, BERT, and RoBERTa - Address challenges such as class imbalance, multilingual variation, and aspect-level sentiment - Evaluate sentiment systems using semantic similarity, temporal trends, and domain-specific metrics This course is ideal for NLP practitioners, data scientists, developers, and applied researchers aiming to build robust, ethical, and production-ready sentiment analysis systems. A basic understanding of Python, NLP fundamentals, and machine learning is recommended. Join us to learn how tokenization and sentiment analysis power the next generation of intelligent language technologies.

Навыки, которые вы освоите

EmbeddingsData EthicsNatural Language ProcessingResponsible AIModel EvaluationData CleansingText MiningMachine Learning AlgorithmsApplied Machine LearningArtificial Intelligence and Machine Learning (AI/ML)Classification AlgorithmsTime Series Analysis and ForecastingUnified Modeling LanguageUnstructured DataFine-tuningLarge Language ModelingData ProcessingData AnalysisTransfer LearningDeep Learning

Программа курса

4 модулей · 96 учебных материалов

01Advanced Tokenization and Text Encoding33 материалов

Subword and Byte-Pair Encoding Techniques

Specialization IntroductionВидеоCourse IntroductionВидеоIntroduction to Subword TokenizationВидеоByte-Pair Encoding (BPE) and Unigram Language ModelsВидеоHandling Out-of-Vocabulary (OOV) Words

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Edureka

Преподаватель курса

Advanced Tokenization and Sentiment Analysis
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Обучение на Coursera

≈ 16.2 ч

4 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Индонезийский, Испанский, Японский, Казахский, Венгерский

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Видео
Demonstration: Subword Tokenization in Real-World ScenariosВидео
Subword and Byte-Pair Encoding Techniques: A Practical PerspectiveЧтение
Your NLP Readiness CheckDIALOGUE
Practice Quiz: Subword and Byte-Pair Encoding TechniquesЗадание
Introduce YourselfОбсуждение

Adaptive and Streaming Tokenization

Dynamic Tokenization StrategiesВидеоReal-Time Tokenization in Streaming ApplicationsВидеоTokenization for Low-Resource and Morphologically Rich LanguagesВидеоDemonstration: OOV Words and Transformer Tokenization (BERT and GPT)ВидеоDemonstration: Dynamic and Adaptive TokenizationВидеоReal-Time and Domain-Aware NLP SolutionsЧтениеPractice Quiz: Adaptive and Streaming TokenizationЗадание

Character-Level and Hybrid Embeddings

Character-Level Embeddings with CNNs and RNNsВидеоFastText: Subword Embeddings and Their UtilityВидеоHybrid Embeddings: Combining Character and Word RepresentationsВидеоHybrid Models: Character-CNNs Integrated with TransformersВидеоApplications of Character-Level Modeling in NLP TasksВидеоHandling the Limits of Word-Level RepresentationsЧтениеPractice Quiz: Character-Level and Hybrid EmbeddingsЗадание

Sentence Embeddings and Semantic Similarity

Sentence-BERT and Universal Sentence EncoderВидеоTechniques for Measuring Semantic Similarity: Cosine, Jaccard, EuclideanВидеоSentence Embedding Use Cases in Search and ChatbotsВидеоSentence Embeddings and Semantic Similarity in Applied NLPЧтениеPractice Quiz: Sentence Embeddings and Semantic SimilarityЗадание

Module Wrap-Up and Assessment

Module Summary: Advanced Tokenization and Text EncodingЧтениеFrom Bytes to Meaning: Tokenization and Embeddings in Multilingual NLPЧтениеFrom Text to Tokens: A Knowledge Check-InDIALOGUEKnowledge Check: Advanced Tokenization and Text EncodingЗадание
02Sentiment Analysis – Models, Methods, and Techniques26 материалов

Fundamentals of Sentiment Analysis

Introduction to Sentiment AnalysisВидеоRule-Based Techniques and Sentiment Lexicons (VADER, SentiWordNet)ВидеоPreprocessing Considerations for Sentiment Analysis TasksВидеоLexicon Scoring and Heuristics in Polarity DetectionВидеоDemo - Sentiment Analysis Using VADER, SentiWordNet, and Custom LexiconsВидеоFundamentals of Sentiment Analysis: Lexicons, Rules, and Preprocessing for Polarity DetectionЧтениеPractice Quiz: Fundamentals of Sentiment AnalysisЗадание

Traditional Machine Learning Approaches

Naïve Bayes and Support Vector Machines for Sentiment ClassificationВидеоDimensionality Reduction: Non-Negative Matrix Factorization (NMF)ВидеоTopic Modeling in Sentiment Tasks: Latent Dirichlet Allocation (LDA)ВидеоHandling Imbalanced Sentiment DatasetsВидеоEvaluation Metrics and Semantic MeasuresВидеоFrom Probabilities to Patterns: Classical Machine Learning in Sentiment AnalysisЧтение

Deep Learning for Sentiment Analysis

LSTMs and GRUs for Sequential Sentiment ModelingВидеоAttention Mechanisms in Deep Sentiment ModelsВидеоSentiment Classification with Pretrained BERT ModelsВидеоFine-Tuning Transformer Models for Domain-Specific Sentiment TasksВидеоState-of-the-Art Transformers: RoBERTa, DistilBERT, GPT-Based ApproachesВидеоFew-Shot and Zero-Shot Sentiment Classification Using Instruction-Tuned LLMsВидео

Module Wrap-Up and Assessment

Module Summary: Sentiment Analysis – Models, Methods, and TechniquesЧтениеAnalyzing Emotion at Scale: Rule-Based, Classical, and Deep Learning Approaches to Sentiment AnalysisЧтениеFrom Polarity to Precision: Your Sentiment Analysis ReflectionDIALOGUEKnowledge Check: Sentiment Analysis – Models, Methods, and TechniquesЗадание
03Real-World Applications and Considerations30 материалов

Temporal and Event-Based Sentiment Trends

Tracking Sentiment Trends Over TimeВидеоDetecting Sudden Shifts in OpinionВидеоSentiment Analysis for Public Discourse and Crisis EventsВидеоUse Cases: Social Media Monitoring, Political Event AnalysisВидеоDemonstration: Temporal Sentiment Tracking and Event Impact AnalysisВидеоTracking Sentiment in Motion: Temporal and Event-Based Sentiment AnalysisЧтениеPractice Quiz: Temporal and Event-Based Sentiment TrendsЗадание

Aspect-Based Sentiment Analysis (ABSA)

Introduction to ABSA and Fine-Grained SentimentВидеоAspect Extraction Using Machine LearningВидеоAspect-Level Sentiment Classification TechniquesВидеоIntegrating NER with ABSA for Enhanced PrecisionВидеоDemonstration: Aspect Based Sentiment AnalysisВидеоGoing Beyond the Stars: Aspect-Based Sentiment Analysis for Fine-Grained Opinion MiningЧтение

Multilingual and Cross-Lingual Sentiment Analysis

Challenges in Multilingual Sentiment ModelingВидеоLanguage-Agnostic Lexicons and EmbeddingsВидеоCross-Lingual Embeddings: MUSE, LASERВидеоFine-Tuning mBERT and XLM-R for Multilingual TasksВидеоZero-Shot and Few-Shot Multilingual Sentiment TransferВидеоAcross Languages and Borders: Building Sentiment Systems for a Multilingual WorldЧтение

Ethical and Fair Use of Sentiment Models

Bias in Sentiment Models: Gender, Race, CultureВидеоReducing False Negatives and Positives in High-Risk ApplicationsВидеоSentiment Analysis in Sensitive Sectors: Healthcare, Justice, HRВидеоFairness, Accountability, and Transparency in Sentiment ClassificationВидеоBeyond Accuracy: Ethical and Fair Use of Sentiment Analysis SystemsЧтениеPractice Quiz: Ethical and Fair Use of Sentiment ModelsЗадание

Module Wrap-Up and Assessment

Module Summary: Real-World Applications and ConsiderationsЧтениеSentiment at Scale: Temporal, Granular, Multilingual, and Ethical Perspectives in Modern Opinion MiningЧтениеKnowledge Check: Real-World Applications and ConsiderationsЗадание
04Course Wrap-Up and Assessment7 материалов

Course Wrap-up and Assessments

Course Summary: Tokenization and Sentiment AnalysisВидеоFrom Tokens to Trends: A Practical Journey Through Modern Sentiment AnalysisЧтениеThe Final Check-In: Turning NLP Knowledge into ActionDIALOGUEPractice Project: IMDb Sentiment AnalysisЛабораторнаяEnd Course Knowledge Check: Tokenization and Sentiment AnalysisЗаданиеDesigning a Multilingual Sentiment Analysis StrategyЗаданиеDescribe your Learning JourneyОбсуждение
Practice Quiz: Traditional Machine Learning ApproachesЗадание
Context, Context, Context: Deep Learning in Sentiment AnalysisЧтение
Practice Quiz: Deep Learning for Sentiment AnalysisЗадание
Practice Quiz: Aspect-Based Sentiment Analysis (ABSA)Задание
Practice Quiz: Multilingual and Cross-Lingual Sentiment AnalysisЗадание