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Applied Text Analysis with Python
Benjamin Bengfort, Rebecca Bilbro & Tony Ojeda

Frequently Asked Questions about "Applied Text Analysis with Python":

1. What is meant by a "language-aware data product"?
A product that understands and processes language meaningfully, performing tasks like translation, summarisation, and question-answering using computational linguistics.

2. Why is building a domain-specific corpus important for text analysis?
It trains models on domain-relevant text, improving accuracy by capturing specific vocabulary and context.

3. What are the key steps involved in preprocessing and wrangling a text corpus?
Steps include segmentation, content extraction, tokenization, and part-of-speech tagging to prepare text for analysis.

4. How is text vectorised, and why is it a crucial step in text analysis? Converts text into numerical formats (e.g., bag-of-words, embeddings) to enable machine learning models to process it.

5. What is the difference between stemming and lemmatisation in text normalisation?
Stemming trims words to base forms (e.g., "running" to "runn"), while lemmatisation ensures valid base forms (e.g., "run") using context.

6. How can n-grams provide context in text analysis, and how does the choice of 'n' affect the analysis?
N-grams capture word sequences; smaller 'n' values focus on local context, while larger ones provide broader context but may add noise.

7. What role do syntactic parsing and context-free grammars play in understanding the structure of sentences?
Parsing uses grammar rules to analyze sentence structure, revealing word relationships and constructing meaning.

8. How can graph analysis be applied to text data, and what insights can it provide?
Text graphs reveal relationships (e.g., co-occurrence) through analysis, highlighting important terms and semantic networks.