Lesson 7 of 7 · 14 min
Text analytics, NLP and data science tools
Text analytics and NLP let machines read filings, call transcripts, news and central bank speeches at a scale no team can, picking up sentiment shifts and short-term indicators early; data scientists build them with languages such as Python and R and databases such as SQL and NoSQL.
In short
- Fintech applications in investment management include text analytics and NLP, risk analysis and algorithmic trading (computerised trade execution under pre-set rules, using real-time market data).
- Text analytics: computer programs that derive meaning from large, usually unstructured text and voice data; includes automated information retrieval and lexical analysis (word frequency).
- Natural language processing (NLP): computer science + AI + linguistics; tasks include translation, speech recognition, text mining, sentiment analysis and topic analysis; also used in compliance.
- NLP can tag sentiment shifts in analyst reports before a rating change and read subtle signals in central bank language.
- Goal: identify trends and short-term indicators about a company, a stock or an economic event.
- Languages: Python, R, Java, C/C++, Excel VBA. Databases: SQL (structured, on a server), SQLite (structured, embedded), NoSQL (unstructured).
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