NLP & Sentiment Analysis

Level: Intermediate Duration: 90 minutes Download PDF

NLP & Sentiment Analysis

Extracting meaning and emotion from text data.

Learning Outcomes

By completing this topic, you will:

  • Preprocess text data (tokenization, normalization)
  • Build sentiment classifiers
  • Use pre-trained embeddings and transformers
  • Evaluate NLP model performance

Visual Guides

Sentiment Distribution
Sentiment Distribution
Word Embeddings
Word Embeddings
Text Preprocessing
Text Preprocessing

Prerequisites

  • Supervised Learning concepts
  • Basic text processing concepts
  • Understanding of classification metrics

Key Concepts

Text Preprocessing

  1. Tokenization: Split text into words/subwords
  2. Normalization: Lowercase, remove punctuation
  3. Stop word removal: Filter common words
  4. Stemming/Lemmatization: Reduce to root form

Sentiment Analysis Approaches

  • Rule-based: Lexicons with sentiment scores
  • Machine Learning: Train on labeled examples
  • Deep Learning: Transformers (BERT, RoBERTa)

Word Embeddings

Dense vector representations:

  • Word2Vec, GloVe (static embeddings)
  • BERT (contextual embeddings)

When to Use

Sentiment analysis is valuable for:

  • Customer feedback analysis
  • Social media monitoring
  • Brand perception tracking
  • Product review summarization

Common Pitfalls

  • Ignoring domain-specific vocabulary
  • Not handling negation (“not good”)
  • Overlooking sarcasm and irony
  • Using general models on specialized text
  • Ignoring class imbalance in training data

(c) Joerg Osterrieder 2025