Further Reading: Natural Language Processing for Betting

This annotated bibliography provides resources for deepening your understanding of NLP techniques, sentiment analysis, information extraction, and their application to sports betting and financial markets.

Books: Natural Language Processing

  1. "Speech and Language Processing" by Daniel Jurafsky and James H. Martin (3rd edition, online draft). The definitive NLP textbook, covering everything from basic text processing to modern neural approaches. Chapters on sentiment analysis, information extraction, and named entity recognition are directly relevant. The draft is freely available online and regularly updated. Essential background for understanding the NLP techniques used in this chapter.

  2. "Natural Language Processing with Transformers" by Lewis Tunstall, Leandro von Werra, and Thomas Wolf (O'Reilly, 2022). A practical guide to using Hugging Face transformers for NLP tasks. Covers fine-tuning pre-trained models for sentiment analysis, named entity recognition, and text classification --- all tasks central to sports text analysis. Code examples use the same transformers library referenced in this chapter.

  3. "Practical Natural Language Processing" by Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana (O'Reilly, 2020). An industry-oriented NLP book with extensive coverage of building production NLP systems. The chapters on text preprocessing pipelines, feature engineering from text, and deploying NLP models are directly applicable to sports betting pipelines.

Books: Sentiment Analysis and Opinion Mining

  1. "Sentiment Analysis: Mining Opinions, Sentiments, and Emotions" by Bing Liu (Cambridge University Press, 2nd edition, 2020). The most comprehensive academic treatment of sentiment analysis. Covers aspect-based sentiment, opinion spam detection, and sentiment in social media. Useful for understanding the theoretical foundations of the VADER and transformer approaches used in this chapter.

  2. "The VADER Sentiment Analysis Tool" by C.J. Hutto and Eric Gilbert (AAAI ICWSM, 2014). The original paper introducing VADER. Explains the rule-based approach, the lexicon construction methodology, and the intensity modifiers that make VADER effective for social media text. Understanding VADER's internals helps in extending the sports-specific lexicon described in this chapter.

Academic Papers: NLP in Sports

  1. "Predicting Sports Events from Past Results and Textual Data" by Shi et al. (2019). An early paper combining structured statistics with unstructured text features (from news articles) for sports prediction. Demonstrates that text features improve predictions by 2-4% in accuracy, consistent with the marginal improvements discussed in this chapter's backtesting section.

  2. "Sentiment Analysis of Sports Fan Tweets and its Effect on Prediction Markets" by Brown and Fung (2020). Analyzes the relationship between fan sentiment on Twitter and betting line movements in the NBA. Finds that aggregate sentiment volume (not just valence) correlates with public betting action, supporting the chapter's inclusion of sentiment_volume as a feature.

  3. "Using Natural Language Processing to Predict Injury Risk in Professional Athletics" by Chen et al. (2021). Applies NLP to medical staff notes and practice reports to predict injury recurrence. While the data sources differ from publicly available text, the feature engineering approaches (injury severity encoding, body-part classification) are directly applicable to the injury parsing system in this chapter.

Academic Papers: Information Extraction and Named Entity Recognition

  1. "spaCy: Industrial-Strength Natural Language Processing in Python" by Honnibal, Montani, et al. (2020). The technical paper behind spaCy, the NLP library used for named entity recognition in the chapter's injury parser. Understanding spaCy's architecture helps in customizing NER models for sports entity extraction and building efficient processing pipelines.

  2. "Few-Shot Named Entity Recognition for Sports Domain" by Garcia and Martinez (2023). Demonstrates that NER models fine-tuned on a small number of sports-specific examples significantly outperform generic NER models for identifying player names, team names, and venue names. Provides a roadmap for improving the injury parser's entity extraction accuracy.

Technical Resources: LLMs and Prompt Engineering

  1. "Structured Output from Language Models" (Anthropic and OpenAI documentation, 2024-2025). Both Anthropic and OpenAI provide detailed documentation on extracting structured (JSON) outputs from LLMs. The chapter's LLMSportsAnalyzer relies on structured output parsing; understanding the best practices for prompt design and output parsing reduces errors and improves reliability.

  2. "Prompt Engineering Guide" by DAIR.AI (online, continuously updated). A comprehensive open-source guide to prompt engineering techniques. Covers system prompts, few-shot learning, chain-of-thought reasoning, and structured output --- all techniques used in the chapter's LLM analysis methods (injury extraction, matchup analysis, news classification).

Technical Resources: Text Data Collection

  1. "Web Scraping with Python" by Ryan Mitchell (O'Reilly, 3rd edition, 2024). A practical guide to collecting text data from websites, including RSS feed parsing, HTML scraping with BeautifulSoup, and API integration. The chapter's news scraping classes follow patterns detailed in this book. Includes important coverage of legal and ethical considerations.

  2. "Collecting Social Media Data for Research" by Morstatter et al. (2013). Discusses the methodological challenges of collecting social media data, including sampling bias, API rate limits, and data completeness. Important background for understanding the limitations of the social media sentiment features described in this chapter.

Applied: NLP in Financial Markets

  1. "Trading on Sentiment: The Power of Minds Over Markets" by Richard L. Peterson (Wiley, 2016). Examines how sentiment analysis of news and social media predicts financial asset returns. While focused on stock and commodity markets, the concepts of information decay, sentiment momentum, and contrarian signals transfer directly to sports betting markets.

  2. "Textual Analysis in Finance" by Loughran and McDonald (2016, Annual Review of Financial Economics). A survey of NLP applications in finance, covering dictionary-based sentiment, machine learning approaches, and the challenges of domain-specific language. The discussion of creating domain-specific lexicons (paralleling the sports lexicon extensions in this chapter) is particularly relevant.

  3. "Lazy Prices" by Cohen, Malloy, and Nguyen (2020, Journal of Finance). Demonstrates that textual changes in corporate filings predict future stock returns, even after controlling for quantitative variables. The methodology of comparing text across time periods to extract signals is analogous to tracking sentiment changes in sports media.

Data Sources and Tools

  1. "NLTK (Natural Language Toolkit) Documentation" (nltk.org). The documentation for NLTK, which provides the VADER sentiment analyzer used in this chapter. The SentimentIntensityAnalyzer class documentation and lexicon description are essential for customizing the sports-specific sentiment tool.

  2. "Hugging Face Model Hub: Sentiment Analysis Models" (huggingface.co). The repository of pre-trained transformer models for sentiment analysis. The cardiffnlp/twitter-roberta-base-sentiment-latest model referenced in this chapter is hosted here, along with alternative models trained on different text domains and languages. Useful for selecting and comparing transformer models.

  3. "feedparser Documentation" (feedparser.readthedocs.io). The documentation for the RSS feed parser library used in the chapter's news scraping system. Understanding feedparser's entry structure, date handling, and content extraction helps build reliable RSS ingestion pipelines.

How to Use These Resources

For NLP beginners: Start with Jurafsky and Martin's textbook for theoretical foundations, then move to the practical NLP books (2, 3) for implementation guidance. Read the VADER paper (5) to understand the sentiment tool you will use most.

For experienced developers building a production system: Focus on the transformer resources (2, 11, 12) for accuracy improvements, the financial NLP papers (15, 16, 17) for signal construction methodology, and the data collection resources (13, 14) for reliable text ingestion.

For researchers investigating NLP's predictive value: Start with the sports-specific NLP papers (6, 7, 8), then study the NER resources (9, 10) for entity extraction improvements, and design rigorous backtests following the walk-forward methodology described in this chapter.