FIELD: physics.
SUBSTANCE: custom dictionary is obtained, containing a list of tokens that refer to the target entity or aspect related to the target entity. A semantic-syntactic analysis of a text part in natural language is performed with the user dictionary to obtain a set of semantic-syntactic structures representing a text part in natural language. The classifier function for determining tonalities associated with one or more aspectual terms is computed using the text characteristics obtained in the semantic-syntactic analysis and a report is created that includes aspect terms and key aspects related to the aspectual terms.
EFFECT: expansion of the technical means arsenal for the sentiment analysis at the level of aspects.
20 cl, 21 dwg
| Title | Year | Author | Number |
|---|---|---|---|
| SENTIMENT ANALYSIS AT THE LEVEL OF ASPECTS USING METHODS OF MACHINE LEARNING | 2016 |
|
RU2657173C2 |
| USE OF DEPTH SEMANTIC ANALYSIS OF TEXTS ON NATURAL LANGUAGE FOR CREATION OF TRAINING SAMPLES IN METHODS OF MACHINE TRAINING | 2016 |
|
RU2636098C1 |
| MULTI STAGE RECOGNITION OF THE REPRESENT ESSENTIALS IN TEXTS ON THE NATURAL LANGUAGE ON THE BASIS OF MORPHOLOGICAL AND SEMANTIC SIGNS | 2016 |
|
RU2619193C1 |
| DEFINITION OF CONFIDENCE DEGREES RELATED TO ATTRIBUTE VALUES OF INFORMATION OBJECTS | 2016 |
|
RU2640297C2 |
| METHOD OF EXTRACTING FACTS FROM TEXTS ON NATURAL LANGUAGE | 2016 |
|
RU2637992C1 |
| EXTRACTION OF INFORMATION USING ALTERNATIVE VARIANTS OF SEMANTIC-SYNTACTIC ANALYSIS | 2016 |
|
RU2646386C1 |
| SELECTION OF TEXT CLASSIFIER PARAMETER BASED ON SEMANTIC CHARACTERISTICS | 2016 |
|
RU2628431C1 |
| EXTRACTING INFORMATION OBJECTS WITH THE HELP OF A CLASSIFIER COMBINATION | 2017 |
|
RU2679988C1 |
| CLASSIFICATION OF TEXTS ON NATURAL LANGUAGE BASED ON SEMANTIC SIGNS | 2016 |
|
RU2628436C1 |
| USING VERIFIED BY USER DATA FOR TRAINING MODELS OF CONFIDENCE | 2016 |
|
RU2646380C1 |
