Method and system for sentiment analysis of information
AssigneeHithink Royalflush Information Network Co., Ltd.
InventorsZheng Yi, Wei Xia
Life of this patent
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Abstract
One aspect of the present disclosure relates to a method of sentiment analysis based on ambiguity analysis, which includes analyzing information with the sentiment analysis models and the ambiguity analysis models. Another aspect of the present disclosure relates to a method of training the sentiment analysis models and ambiguity analysis models, which includes acquiring information, constructing lexicons, conducting sentiment analysis and ambiguity analysis with said lexicons, acquiring corpus, and training models, etc. Meanwhile, another aspect of the present disclosure relates to a system of sentiment analysis, which includes input, and output modules, acquisition modules, processing modules and database.
Claims19
A system, comprising:
- a non-transitory computer readable storage medium storing executable instructions;
- and
- at least one processor in communication with the non-transitory computer readable storage medium, wherein when executing the executable instructions, the at least one processor is configured to cause the system to:
- obtain a user input from a device, wherein the device obtains the user input via an interface of the device, wherein the interface of the device includes a conversational interface;
- retrieve information including a plurality of pieces from a database according to the user input, wherein each piece of the information contains the user input and is marked by the system with a sentiment type of one or more sentiment types;
- classify the information based on the one or more sentiment types to obtain classifying results;
- and
- send, over the network, the information and the classifying results to the device, wherein the device displays the received information on the conversational interface based on the classifying results,
- wherein the sentiment type is determined using a sentiment analyzer, the sentiment analyzer comprising at least one of a Maximum entropy algorithm training model or a support vector machine (SVM) model.
A method implemented on a device comprising a storage and a processor, comprising:
- obtaining a user input via an interface of the device, wherein the interface of the device includes a conversational interface;
- sending, over a network, the user input to a system;
- receiving, over the network from the system, information including a plurality of pieces, retrieved by the system from a database according to the user input, wherein each piece of the information contains the user input and is marked by the system with a sentiment type of one or more sentiment types;
- receiving, over the network from the system, classifying results obtained by the system by classifying the information based on the one or more sentiment types;
- and
- displaying the received information on the conversational interface based on the classifying results,
- wherein the sentiment type is determined using a sentiment analyzer, the sentiment analyzer comprising at least one of a Maximum entropy algorithm training model or a support vector machine (SVM) model.
A non-transitory computer readable medium embodying a computer program product, the computer program product comprising instructions configured to cause a device to:
- obtaining a user input via an interface of the device, wherein the interface of the device includes a conversational interface;
- sending, over a network, the user input to a system;
- receiving, over the network from the system, information including a plurality of pieces, retrieved by the system from a database according to the user input, wherein each piece of the information contains the user input and is marked by the system with a sentiment type of one or more sentiment types;
- receiving, over the network from the system, classifying results obtained by the system by classifying the information based on the one or more sentiment types;
- and
- displaying the received information on the conversational interface based on the classifying results,
- wherein the sentiment type is determined using a sentiment analyzer, the sentiment analyzer comprising at least one of a Maximum entropy algorithm training model or a support vector machine (SVM) model.
Dependencies are inferred from the claim text.