13.4 Sentiment analysis and text classification

Text processing and analysis are crucial in understanding emotions and categorizing content. Sentiment analysis and text classification help businesses gauge customer feedback and organize information. These techniques face challenges like language ambiguity and sarcasm detection.

Preprocessing steps like tokenization and vectorization prepare text for deep learning models. CNNs and LSTMs, along with attention mechanisms and transfer learning, power sentiment analysis. Performance metrics like accuracy and F1 score help evaluate model effectiveness.

Text Processing and Analysis

Concepts of sentiment analysis

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• Sentiment analysis determines emotional tone or attitude in text used for customer feedback analysis and social media monitoring
• Text classification assigns predefined categories to documents applied in spam detection and topic categorization
• Sentiment analysis focuses on emotional content while text classification deals with broader categorization tasks
• Challenges include ambiguity in language, sarcasm detection, and handling multiple languages (English, Spanish, Mandarin)

Text preprocessing for classification

• Tokenization breaks text into individual words or subwords
• Lowercasing converts all text to lowercase for consistency
• Remove punctuation and special characters
• Handle stop words by removing or keeping common words (the, and, is)
• Stemming or lemmatization reduces words to base form (running → run)
• Text vectorization techniques:
  1. Bag of Words (BoW) represents text as word frequency vector
  2. TF-IDF weights words based on importance in document and corpus
  3. Word embeddings provide dense vector representations (Word2Vec, GloVe)
  4. Character-level encodings represent text as character sequences
• Handle out-of-vocabulary words and pad/truncate sequences for fixed-length input

Deep learning models for sentiment

• Convolutional Neural Networks (CNNs) for text:
  • 1D convolutions process sequence data
  • Pooling operations (max pooling, average pooling) reduce dimensionality
  • Multiple filter sizes capture different n-gram patterns
• Long Short-Term Memory (LSTM) networks:
  • Recurrent architecture processes sequential data
  • Gating mechanisms (input gate, forget gate, output gate) control information flow
  • Bidirectional LSTMs capture context from both directions
• Embedding layers learn word representations
• Dropout and regularization prevent overfitting
• Attention mechanisms focus on important input parts
• Transfer learning fine-tunes pre-trained models (BERT, GPT)
• Hyperparameter tuning optimizes learning rate, batch size, and network architecture

Performance metrics in text analysis

• Confusion matrix shows true positives, true negatives, false positives, false negatives
• Accuracy measures overall prediction correctness: $Accuracy = \frac{TP + TN}{TP + TN + FP + FN}$
• Precision calculates proportion of correct positive predictions: $Precision = \frac{TP}{TP + FP}$
• Recall determines proportion of actual positives identified: $Recall = \frac{TP}{TP + FN}$
• F1 score computes harmonic mean of precision and recall: $F1 = 2 * \frac{Precision * Recall}{Precision + Recall}$
• ROC curve and AUC evaluate binary classification performance
• Cross-validation techniques (k-fold, stratified k-fold) assess model generalization
• Handle class imbalance through oversampling, undersampling, or adjusting class weights