Capturing a subjective viewpoint of a financial market analyst via a machine-learned model
AssigneeGreen Key Technologies, Inc.
InventorsAnthony TASSONE, Tejas SHASTRY
Life of this patent
Estimating the term…
Abstract
Systems and methods herein provide for establishing a subjective viewpoint in text. In one embodiment, a method includes identifying intents and metrics in each of a plurality of texts, calculating a sentiment score for each text based on the identified intents and metrics of each text, and calculating a disfluency score for each text to weight the sentiment score of each text. The method also includes training the machine learning model with the texts, and processing a subsequent text through the trained machine learning model to determine a sentiment score of the subsequent text.
Claims20
A method of establishing a subjective viewpoint of a text with a machine learning model, the method comprising:
- identifying intents and metrics in each of a plurality of texts;
- calculating a sentiment score for each text based on the identified intent and metrics of each said text;
- calculating a disfluency score for each said text to weight the sentiment score of each said text;
- training the machine learning model with the texts;
- and
- processing a subsequent text through the trained machine learning model to determine a sentiment score of the subsequent text.
A system for establishing a subjective viewpoint of a text with a machine learning model, the system comprising:
- a database operable to store a plurality of texts that have each been labeled with identified intents and metrics;
- a natural language processor operable to calculate a sentiment score for each text based on the identified intent labels and metrics of each said text, calculate a disfluency score for each said text to weight the sentiment score of each said text, train the machine learning model with the texts, and to process a subsequent text through the trained machine learning model to determine a sentiment score of the subsequent text.
A non-transitory computer readable medium comprising instructions that, when executed by a processor, direct the processor to implement a machine learning model and to establish a subjective viewpoint of a text with the machine learning model, the instructions further directing the processor to:
- identify intents and metrics in each of a plurality of texts;
- calculate a sentiment score for each text based on the identified intent and metrics of each said text;
- calculate a disfluency score for each said text to weight the sentiment score of each said text;
- train the machine learning model with the texts;
- and
- process a subsequent text through the trained machine learning model to determine a sentiment score of the subsequent text.
Dependencies are inferred from the claim text.