Maximum-entropy model and TF-IDF (Term Frequency-Inverse Document Frequency) feature-based emotion analysis method

A maximum entropy model, TF-IDF technology, applied in semantic analysis, special data processing applications, instruments, etc., can solve problems such as inability to flexibly set the fitness of unknown data and adjust the fitting degree of known data

Inactive Publication Date: 2018-04-20
GLOBAL TONE COMM TECH
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AI Technical Summary

Problems solved by technology

[0006] It is not possible to flexibly set constraints to adjust

Method used

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  • Maximum-entropy model and TF-IDF (Term Frequency-Inverse Document Frequency) feature-based emotion analysis method
  • Maximum-entropy model and TF-IDF (Term Frequency-Inverse Document Frequency) feature-based emotion analysis method
  • Maximum-entropy model and TF-IDF (Term Frequency-Inverse Document Frequency) feature-based emotion analysis method

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[0051] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0052] The application principle of the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0053] Such as figure 1 As shown, the sentiment analysis method based on the maximum entropy model and the TF-IDF feature provided by the embodiment of the present invention includes:

[0054] S101: The maximum entropy model is used as the basic classification model.

[0055] S102: Use the training document set to train the maximum entropy model. When the model converges or the number of iterations reaches a set threshold, the training ends, and the obtained ma...

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Abstract

The invention belongs to the technical field of language analysis, and discloses a maximum-entropy model and TF-IDF (Term Frequency-Inverse Document Frequency) feature-based emotion analysis method. Amaximum-entropy model is used as a basic classification model; and a training document set is utilized to train the maximum-entropy model, training ends when the model converges or the number of iterations reaches a set threshold value, and an obtained maximum-entropy model is used for prediction of emotion classification. According to the method, classification information in a large-scale corpus can be deeply mined, and the maximum-entropy classification model is combined to realize fine-grained emotion analysis of textual text. Through testing, an F value of the method in the invention onthe fine-grained emotion analysis problem of 5-classification reaches 62.9%, and is increased by more than 10% as compared with F values of traditional methods of combining feature engineering and machine learning.

Description

technical field [0001] The invention belongs to the technical field of language analysis, in particular to an emotion analysis method based on a maximum entropy model and TF-IDF features. Background technique [0002] Sentiment analysis, also known as sentiment orientation analysis, refers to the automatic recognition of the emotional orientation expressed in natural language texts through computer algorithms. It is an important hot spot in the research and application of artificial intelligence and machine learning. The rapid development of mobile Internet, e-commerce, social network, artificial intelligence and other technologies has greatly expanded the boundaries of information and enriched the breadth and depth of data. Big data is becoming a basic and strategic resource for enterprise development and social development. It is an important application of sentiment analysis technology to dig out the public's emotional tendencies towards events or subjects from the massiv...

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Application Information

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IPC IPC(8): G06F17/27G06F17/30
CPCG06F16/353G06F40/216G06F40/30
Inventor 李世奇程国艮
Owner GLOBAL TONE COMM TECH
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