IP Library Patent Application 14509311
Patent Application
App. No. 14/509,311

TEXT DATA SENTIMENT ANALYSIS METHOD

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Quick Facts
Patent No.
US None
App. No.
14/509,311
Abstract

A method and system for text data analysis by performing deep syntactic and semantic analysis of text data and extracting entities and facts from the text data based on the results of deep syntactic and semantic analysis, including extraction of sentiments using a sentiment lexicon constructed upon a semantic hierarchy. The data analysis can include determining sign of the extracted sentiment, aggregate function of the text data, analyzing social mood, and classifying the text data.

Claims (34)

1 . A method of text data analysis, including:

obtaining text data;

performing deep syntactic and semantic analysis of text data;

extracting entities and facts from text data based on the results of deep syntactic and semantic analysis, including

extraction of sentiments using a sentiment lexicon constructed upon a semantic hierarchy.

2 . The method of claim 1 , further including the step of determining the sign of the extracted sentiments.

3 . The method of claim 1 , further including the step of determining the aggregate function of text data.

4 . The method of claim 1 , further including the step of identifying social networks based on the extracted entities and facts.

5 . The method of claim 1 , further including the step of identifying topics based on the extracted entities and facts.

6 . The method of claim 1 , further including the step of analyzing the social mood based on the extracted sentiments.

7 . The method of claim 1 , further including the step of classifying text data based on the extracted sentiments.

8 . A system of text data analysis, including:

one or more processors adjusted for:

obtaining text data;

performing deep syntactic and semantic analysis of text data;

extracting entities and facts from text data based on the results of deep syntactic and semantic analysis, including

extraction of sentiments using a sentiment lexicon constructed upon a semantic hierarchy.

9 . The system of claim 7 , further including the step of determining the sign of the extracted sentiments.

10 . The system of claim 7 , further including the step of determining the aggregate function of text data.

11 . The system of claim 7 , further including the step of identifying social networks based on the extracted entities and facts.

12 . The system of claim 7 , further including the step of identifying topics based on the extracted entities and facts.

13 . The system of claim 7 , further including the step of analyzing the social mood based on the extracted sentiments.

14 . The system of claim 7 , further including the step of classifying text data based on the extracted sentiments.

15 . A non-volatile machine-readable information storage medium containing the following instructions:

obtaining text data;

performing deep syntactic and semantic analysis of text data;

extracting entities and facts from text data based on the results of deep syntactic and semantic analysis, including

extraction of sentiments using a sentiment lexicon constructed upon a semantic hierarchy.

16 . The non-volatile machine-readable information storage medium of claim 13 , further including the step of determining the sign of the extracted sentiments.

17 . The non-volatile machine-readable information storage medium of claim 13 , further including the step of determining the aggregate function of text data.

18 . The non-volatile machine-readable information storage medium of claim 13 , further including the step of identifying social networks based on the extracted entities and facts.

19 . The non-volatile machine-readable information storage medium of claim 13 , further including the step of identifying topics based on the extracted entities and facts.

20 . The non-volatile machine-readable information storage medium of claim 13 , further including the step of analyzing the social mood based on the extracted sentiments.

21 . The non-volatile machine-readable information storage medium of claim 13 , further including the step of classifying text data based on the extracted sentiments.

Assignments (4)
CORRECTIVE ASSIGNMENT TO CORRECT THE ASSIGNOR DOC. DATE PREVIOUSLY RECORDED AT REEL: 042706 FRAME: 0279. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT. Recorded Aug 25, 2017
From: ABBYY INFOPOISK LLC
To: ABBYY PRODUCTION LLC
Reel/Frame 043676/0232 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 6, 2017
From: ABBYY INFOPOISK LLC
To: ABBYY PRODUCTION LLC
Reel/Frame 042706/0279 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 9, 2015
From: YAN, DAVID YEVGENIEVICH; TYURIN, ANTON YEVGENIEVICH; MIKHAYLOV, MAKSIM BORISOVICH; DANIELYAN, TATIANA VLADIMIROVNA; LOKOTILOVA, OLGA VLA
To: ABBYY INFOPOISK LLC
Reel/Frame 035115/0842 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 7, 2014
From: TYURIN, ANTON YEVGENIEVICH; MIKHAYLOV, MAKSIM BORISOVICH; DANIELYAN, TATIANA VLADIMIROVNA; LOKOTILOVA, OLGA VLADIMIROVNA
To: ABBYY INFOPOISK LLC
Reel/Frame 034126/0906 →