Sentiment Analysis Training in Astana, Kazakhstan

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.

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.