System and method for customized sentiment signal generation through machine learning based streaming text analytics
First Claim
1. A computer implemented method, comprising:
- extracting, by the computer processor, one or more sentiment signals comprising weighted, customized sentiment metrics for words and expressions from a library comprising one or more research reports from one or more sources;
calculating normalized profiles for the one or more sources of data based on one or more of geography, sector, analyst, company, and streaming real time data feedback;
normalizing, by the computer processor, the one or more sentiment signals based on text positioning, sentence structure, and data source;
calculating an overall sentiment score;
receiving, by the computer processor, one or more data streams;
receiving, by the computer processor, a set of weighted, customized metrics based on the one or more sentiment signals wherein the set of weighted, customized metrics is received from a dynamically updated database that assigns and updates sentiments based on empirical data, customizes factors based on analyst, sectors, and geography, and comprises numeric sensitivity factors for numeric expressions;
applying the set of weighted, customized metrics to the one or more data streams;
outputting, by the computer processor, a customized data stream that is a result of the application of the set of weighted, customized metrics;
ranking the one or more sentiment signals based on the overall sentiment score; and
providing a visualization of the ranking including a link to each source associated with the one or more sentiment signals.
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Accused Products
Abstract
Systems and methods may provide customized integrated indexes and visualization. Sentiment analytics may be based on natural language processing techniques. Users may select from among a range of indexes that reflect a variety of sources. Text scoring metrics or indices may incorporate frequency of mention, link to broker action, sentence location of first mention, etc. Depending on the temporal and sentiment characteristics of interest, the user may select from a range of news sources, research reports, analysts, social media sources, and may assign a customized weight value to each source. The scores may then be merged. After scoring, the user may be presented with news links directly from sentiment indexes (e.g., from top ranking in terms of sentiment scores, etc.). Advanced visualization capabilities may provide output for users to assist in decision making processes.
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Citations
65 Claims
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1. A computer implemented method, comprising:
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extracting, by the computer processor, one or more sentiment signals comprising weighted, customized sentiment metrics for words and expressions from a library comprising one or more research reports from one or more sources; calculating normalized profiles for the one or more sources of data based on one or more of geography, sector, analyst, company, and streaming real time data feedback; normalizing, by the computer processor, the one or more sentiment signals based on text positioning, sentence structure, and data source; calculating an overall sentiment score; receiving, by the computer processor, one or more data streams; receiving, by the computer processor, a set of weighted, customized metrics based on the one or more sentiment signals wherein the set of weighted, customized metrics is received from a dynamically updated database that assigns and updates sentiments based on empirical data, customizes factors based on analyst, sectors, and geography, and comprises numeric sensitivity factors for numeric expressions; applying the set of weighted, customized metrics to the one or more data streams; outputting, by the computer processor, a customized data stream that is a result of the application of the set of weighted, customized metrics; ranking the one or more sentiment signals based on the overall sentiment score; and providing a visualization of the ranking including a link to each source associated with the one or more sentiment signals. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22)
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23. A computer implemented method, comprising:
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extracting, by computer processor, one or more sentiment signals comprising a set of weighted, customized sentiment metrics for words and expressions from a source document; calculating a normalized profile for the source document based on one or more of geography, sector, analyst, and company; cross-checking each of the one or more sentiment signals for consistency within the source document; applying the normalized profile to the one or more sentiment signals; normalizing, by the computer processor, the one or more sentiment signals based on text positioning, sentence structure, and document type; calculating an overall sentiment score; receiving, by the computer processor, one or more data streams; receiving, by the computer processor, a set of weighted, customized metrics based on the one or more sentiment signals wherein the set of weighted, customized metrics is received from a dynamically updated database that assigns and updates sentiments based on empirical data, customizes factors based on analyst, sectors, and geography, and comprises numeric sensitivity factors for numeric expressions; applying the set of weighted, customized metrics to the one or more data streams; outputting, by the computer processor, a customized data stream that is a result of the application of the set of weighted, customized metrics; and ranking the one or more sentiment signals based on the overall sentiment score; and providing a visualization of the ranking including a link to each source associated with the one or more sentiment signals.
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24. A computer implemented method, comprising:
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extracting, by the computer processor, one or more sentiment signals comprising weighted, customized sentiment metrics for words and expressions from a live streaming data source comprising one or more sources of data delivered over a computer network; calculating normalized profiles for the one or more sources of data based on one or more of geography, sector, analyst, company, and streaming real time data feedback; normalizing, by the computer processor, the one or more sentiment signals based on text positioning, sentence structure, and data source; calculating an overall sentiment score; receiving, by the computer processor, one or more data streams; receiving, by the computer processor, a set of weighted, customized metrics based on the one or more sentiment signals wherein the set of weighted, customized metrics is received from a dynamically updated database that assigns and updates sentiments based on empirical data, customizes factors based on analyst, sectors, and geography, and comprises numeric sensitivity factors for numeric expressions; applying the set of weighted, customized metrics to the one or more data streams; outputting, by the computer processor, a customized data stream that is a result of the application of the set of weighted, customized metrics; ranking the one or more sentiment signals based on the overall sentiment score; and providing a visualization of the ranking including a link to each source associated with the one or more sentiment signals. - View Dependent Claims (25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44)
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45. A system, comprising:
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a processor; and a memory comprising computer-readable instructions which when executed by the processor cause the processor to perform the steps comprising; extracting one or more sentiment signals comprising weighted, customized sentiment metrics for words and expressions from a library comprising one or more sources of data; calculating normalized profiles for the one or more sources of data based on one or more of geography, sector, analyst, company, and streaming real time data feedback; normalizing the one or more sentiment signals based on text positioning, sentence structure, and document type; calculating an overall sentiment score; receiving one or more data streams; receiving a set of weighted, customized metrics based on the one or more sentiment signals wherein the set of weighted, customized metrics is received from a dynamically updated database that assigns and updates sentiments; applying the set of weighted, customized metrics to the one or more data streams; outputting a customized data stream that is a result of the application of the set of weighted, customized metrics; ranking the one or more sentiment signals based on the overall sentiment score; and providing a visualization of the ranking including a link to each actual source associated with the one or more sentiment signals. - View Dependent Claims (46, 47, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 62, 63, 64, 65)
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48. The method of 45, wherein the weighted, customized metrics are updated in real time in the dynamically updated database through machine learning.
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55. The system of 45, further comprising:
performing a cross check of the one or more sentiment signals within the library for consistency.
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60. A system, comprising:
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a processor; and a memory comprising computer-readable instructions which when executed by the processor cause the processor to perform the steps comprising; parsing one or more sources of data for sentiment and source attributes; extracting source attributes from the one or more sources of data; fusing the source attributes with the sentiment; linking the sentiment information with its original source, related content, and other related material; weighting the sentiment information based on importance and relevance; storing, in a dynamically weighted dictionary, terms associated with sentiment and concept; outputting an object with dynamic sentiment and attributes; receiving one or more data streams; receiving a set of weighted, customized metrics based on one or more sentiment signals wherein the set of weighted, customized metrics is received from a dynamically updated database that assigns and updates sentiments based on empirical data, customizes factors based on analyst, sectors, and geography, and comprises numeric sensitivity factors for numeric expressions; applying the set of weighted, customized metrics to the one or more data streams; and outputting a customized data stream that is a result of the application of the set of weighted, customized metrics. - View Dependent Claims (61)
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Specification