Sentiment Analysis Training in Astana

Sentiment Analysis Training in Astana, Kazakhstan

Participants will explore key concepts, including natural language processing (NLP), machine learning algorithms, and sentiment classification models.

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Sentiment Analysis Training is a professional training program delivered by ProgNXT in Astana, Kazakhstan. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this Sentiment Analysis Training course with hands-on, instructor-led sessions. This training on Sentiment Analysis provides a comprehensive introduction to the techniques and tools used in extracting and analyzing opinions,...

Expert Panel

Designed by the ProgNXT AI & Data Science Expert Panel, specializing in Generative AI, Machine Learning, and ChatGPT applications

ProgNXT AI & Data Science Expert Panel

Course Code: ZDL20
14 Hrs
Last Updated: April 2026

Course Rating5/5

This training on Sentiment Analysis provides a comprehensive introduction to the techniques and tools used in extracting and analyzing opinions, emotions, and sentiments from text data. Participants will explore key concepts, including natural language processing (NLP), machine learning algorithms, and sentiment classification models. The course will provide hands-on experience using popular libraries and tools such as Python's NLTK, TextBlob, and VaderSentiment. Attendees will learn how to apply sentiment analysis to various data sources such as product reviews, social media content, and customer feedback.

Welcome to the official Sentiment Analysis Training certification program in Astana, Kazakhstan. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge locally. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 14 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Participants should have:

Basic knowledge of programming, preferably in Python.
Understanding of basic machine learning concepts (e.g., supervised learning, classification).
No prior experience in sentiment analysis is required, but familiarity with text processing and NLP will be beneficial.

By the end of this training, participants will:

Understand the core concepts and methods of sentiment analysis.
Learn how to preprocess and clean textual data for sentiment classification.
Be proficient in using popular Python libraries (e.g., NLTK, TextBlob, VaderSentiment) for sentiment analysis.
Understand the challenges of sentiment analysis, such as handling sarcasm, ambiguity, and context.
Apply sentiment analysis to real-world datasets and derive actionable insights.
Learn how to visualize sentiment results and make data-driven decisions.

Upon successful completion of this course, participants will be able to:

Preprocess and clean text data for sentiment analysis.
Use machine learning algorithms for text classification in sentiment analysis tasks.
Implement sentiment analysis using Python libraries such as NLTK, TextBlob, and VaderSentiment.
Analyze sentiment scores for social media posts, customer reviews, and other forms of unstructured text data.
Visualize sentiment trends and insights using data visualization tools.
Fine-tune models for better accuracy in sentiment analysis applications.

Participants will be assessed on:

Text preprocessing techniques: Ability to clean and prepare text data for sentiment analysis.
Knowledge of machine learning algorithms for sentiment classification.
Proficiency in using Python libraries for sentiment analysis (NLTK, TextBlob, VaderSentiment).
Ability to analyze sentiment data and draw meaningful insights.
Application of data visualization techniques to present sentiment analysis results.
Successful completion of hands-on projects (e.g., analyzing sentiment from social media posts or product reviews).

Upon successful completion of the training, participants will receive a ProgNXT Certificate of Completion, acknowledging their knowledge and proficiency in Sentiment Analysis.

Introduction to Sentiment Analysis and Text Preprocessing

Session 1: Introduction to Sentiment Analysis
- What is sentiment analysis?
- Applications and use cases: Social media monitoring, brand sentiment, customer feedback analysis.
- Overview of Natural Language Processing (NLP) and its relevance in sentiment analysis.
Session 2: Text Preprocessing and Feature Extraction
- Text cleaning techniques: Tokenization, stopword removal, stemming, lemmatization.
- Feature extraction methods: Bag-of-Words (BoW), TF-IDF (Term Frequency-Inverse Document Frequency).
Session 3: Introduction to Sentiment Analysis Models
- Overview of lexicon-based models (e.g., VaderSentiment, TextBlob).
- Introduction to machine learning-based models for sentiment classification (e.g., Naive Bayes, SVM).

Advanced Techniques in Sentiment Analysis and Visualization

Session 4: Machine Learning for Sentiment Analysis
- Overview of supervised learning algorithms: Naive Bayes, Logistic Regression, SVM.
- Feature engineering for sentiment analysis: Using word embeddings (e.g., Word2Vec, GloVe).
Session 5: Handling Challenges in Sentiment Analysis
- Dealing with sarcasm, irony, and ambiguous text.
- Identifying and addressing domain-specific sentiment (e.g., product reviews vs. movie reviews).
- Handling multilingual sentiment analysis.
Session 6: Visualizing Sentiment Data and Drawing Insights
- Introduction to data visualization techniques: Bar charts, pie charts, word clouds.
- Visualizing sentiment trends over time and across different datasets.
- Hands-on

This training program equips participants with the practical skills to implement sentiment analysis on various text datasets. By combining theory with hands-on exercises, the course prepares attendees to analyze textual data for extracting meaningful insights and applications in customer feedback, social media sentiment, and more.

Participants will receive:

Course slides with detailed explanations and code samples.
Datasets for sentiment analysis tasks (e.g., product reviews, social media posts).
Links to relevant resources such as tutorials, blog posts, and research papers.
Code repositories with additional examples and best practices in sentiment analysis.

Participants will be evaluated through a hands-on project and a final quiz.
ProgNXT Certificates of completion will be awarded to participants who successfully complete the assessments.

Sentiment Analysis Training Events in astana

Online Events

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More Upcoming Session Dates

October 2026

📍 Astana, Kazakhstan

26th Oct 2026→27th Oct 2026

November 2026

📍 Astana, Kazakhstan

2nd Nov 2026→3rd Nov 2026

🌐 Online Instructor-Led

7th Nov 2026→8th Nov 2026

🌐 Online Instructor-Led

22nd Nov 2026→23rd Nov 2026

📍 Astana, Kazakhstan

27th Nov 2026→28th Nov 2026

December 2026

🌐 Online Instructor-Led

4th Dec 2026→5th Dec 2026

📍 Astana, Kazakhstan

11th Dec 2026→12th Dec 2026

📍 Astana, Kazakhstan

21st Dec 2026→22nd Dec 2026

🌐 Online Instructor-Led

27th Dec 2026→28th Dec 2026

Events for other courses in Astana, Kazakhstan

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Sentiment Analysis Training in Astana