Natural language processing techniques using joint sentiment-topic modeling
US11494565B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 6, 2020 |
| Grant date | Nov 8, 2022 |
| Priority date | — |
| Expiry date | May 13, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
There is a need for more effective and efficient natural language processing (NLP) solutions. This need can be addressed by, for example, solutions for performing NLP-based document prioritization by utilizing joint sentiment-topic (JST) modeling. In one example, a method comprises identifying a JST latent distribution of the digital document that describes topic designation probabilities and sentiment designation probabilities for the digital document; determining, by processing the topic designation probabilities, a document-topic entropy measure for the digital document; determining, by processing the sentiment designation probabilities, a sentiment-topic entropy measure for the digital document; determining, by processing per-word inverse domain frequency measures for the digital, a document popularity measure for the digital document; generating the predicted document priority score based on the document-topic entropy measure, the sentiment-topic entropy measure, and the document popularity measure; and performing one or more prediction-based actions based on the predicted document priority score.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.