Uncover the Stories That Drive Markets
NarrativeMiner
class in the bigdata-research-tools
package systematically tracks narrative evolution across multiple document types using unstructured data from news, transcripts, and filings. Built for analysts and investment professionals, it transforms scattered narrative signals into quantified trend intelligence and identifies timing patterns across different information sources.
NarrativeMiner
combines multi-source content retrieval, temporal narrative tracking, and cross-source comparative analysis to deliver:
README.md
.README.md
main_narratives
): Specific narrative sentences related to AI bubble concernsllm_model
): The LLM model used to label search result document chunks and generate summariesstart_date
and end_date
): The date range over which to run the analysisrerank_threshold
): By setting this value, you’re enabling the cross-encoder which reranks the results and selects those whose relevance is above the percentile you specify (0.7 being the 70th percentile). More information on the re-ranker can be found here.document_limit
): The maximum number of documents to return per query to Bigdata API.fiscal_year
): If the document type is transcripts or filings, fiscal year needs to be specifiedfreq
): The frequency of the date ranges to search over. Supported values:
Y
: Yearly intervals.M
: Monthly intervals.W
: Weekly intervals.D
: Daily intervals.NarrativeMiners
provide a comprehensive automated framework for tracking narrative evolution across multiple information sources simultaneously. By systematically combining advanced information retrieval with temporal analysis, this workflow transforms scattered narrative signals into structured intelligence for strategic decision-making.
Through the automated analysis of AI bubble concerns across news, earnings calls, and regulatory filings, you can:
NarrativeMiners
automate the research process while maintaining the depth required for professional analysis. The cross-source methodology ensures comprehensive coverage of narrative development, making it an invaluable tool for systematic market narrative intelligence in dynamic information environments.
This analysis demonstrates how systematic narrative mining across multiple document types provides richer insights than analyzing any single source in isolation, revealing the complete lifecycle of market narratives from emergence to institutional response.