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Each chunk returned by the Search Service has a sentiment parameter with a value ranging from -1.00 to 1.00, indicating how that chunk text might impact the market and whether it will have a positive or negative effect.

Sentiment Model

The Bigdata.com Sentiment Model is an advanced language model designed to quantify the market impact of financial and business news with precision and consistency. Using BERT-like transformer-based architecture, it captures contextual cues to evaluate how news content may influence market perception and investor behavior. The model produces a continuous sentiment impact score, ranging from -1, for strongly negative to 1, for strongly positive. This allows users to identify both the direction and intensity of the expected market reactions. The scoring is derived from a large-scale analysis of financially relevant headlines and articles, particularly those related to equities and corporate events. Unlike general-purpose sentiment models, the Bigdata.com Sentiment Model has been specifically trained on financial language, capturing the meaning behind complex expressions or market-specific phrasing. The training data spans from 2014 to 2023.

Examples

Questions

The model was trained mostly to understand economically relevant events when generating sentiment scores, with a focus on equities, but, as for most transformer models pretrained with a large corpus of text, its language understanding goes well beyond that. This means that while there is no specific training for events around people, for example, it will understand the text and apply sentiment analysis based on the wording and context.
The sentiment model calculates and persists the chunk sentiment when Bigdata processes documents; therefore, its value is static and not conditioned by any search filter in the request.