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References: Natural Language Processing

  1. Natural Language Processing - Wikipedia - Broad overview of NLP techniques including tokenization, parsing, named entity recognition, sentiment analysis, and modern transformer-based approaches applicable to organizational text analysis.

  2. Sentiment Analysis - Wikipedia - Covers computational approaches to detecting opinion, emotion, and attitude in text. Directly applicable to analyzing employee communication tone, engagement shifts, and organizational culture signals.

  3. Word Embedding - Wikipedia - Explains distributed word representations (Word2Vec, GloVe, FastText) that capture semantic relationships between terms. Used to analyze skill vocabularies, topic similarity, and communication themes in organizational text.

  4. Speech and Language Processing (3rd Edition draft) - Dan Jurafsky and James H. Martin - Prentice Hall (2024) - The standard NLP textbook covering tokenization, NER, sentiment, topic modeling, and transformer architectures. Free draft available online at https://web.stanford.edu/~jurafsky/slp3/.

  5. The Hidden Power of Social Networks - Rob Cross and Andrew Parker - Harvard Business Review Press (2004) - Chapter 6 discusses how communication content analysis complements structural network analysis, identifying "energizing" vs. "de-energizing" communication patterns that anticipate modern sentiment analysis.

  6. Named Entity Recognition - Wikipedia - NER techniques for identifying people, organizations, locations, and domain-specific entities in text. Essential for extracting structured relationship data from unstructured organizational communications.

  7. Topic Model - Wikipedia - Statistical models (LDA, NMF) for discovering abstract topics in document collections. Applicable to categorizing organizational communication themes and tracking topic evolution over time.

  8. Transformer (deep learning architecture) - Wikipedia - Explains the self-attention architecture underlying modern LLMs (BERT, GPT) that power state-of-the-art text understanding for organizational communication analysis.

  9. Text Summarization - Wikipedia - Extractive and abstractive summarization techniques for condensing long documents. Applicable to summarizing meeting transcripts, email threads, and project documentation in organizational contexts.

  10. spaCy NLP Library - Explosion AI - Documentation for the industrial-strength NLP library providing tokenization, NER, dependency parsing, and text classification pipelines commonly used in organizational text analytics.