Advanced Tokenization and Sentiment Analysis
$49
ENROLL NOWCourse Overview
What You'll Learn
- 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.
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.
Course FAQs
Is this an accredited online course?
Accreditation for 'Advanced Tokenization and Sentiment Analysis' is determined by the provider, Edureka. For online college courses or degree programs, we strongly recommend you verify the accreditation status directly on the provider's website to ensure it meets your requirements.
Can this course be used for continuing education credits?
Many of the courses listed on our platform are suitable for professional continuing education. However, acceptance for credit varies by state and licensing board. Please confirm with your board and {course.provider} that this specific course qualifies.
How do I enroll in this online school program?
To enroll, click the 'ENROLL NOW' button on this page. You will be taken to the official page for 'Advanced Tokenization and Sentiment Analysis' on the Edureka online class platform, where you can complete your registration.
