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Emotion detection method and system based on map expressions

A detection method and detection system technology, applied in the field of sentiment analysis, can solve problems such as inability to simply handle, and achieve the effects of saving memory, improving recognition, and reducing risks

Active Publication Date: 2018-05-18
SHENZHEN NAIFEI TECH CO LTD
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  • Application Information

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Problems solved by technology

[0002] With the continuous development of the network and the rapid growth of social platforms, the Internet is flooded with a large amount of unstructured text data, which contains a lot of valuable information that cannot be easily processed and extracted

Method used

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  • Emotion detection method and system based on map expressions
  • Emotion detection method and system based on map expressions
  • Emotion detection method and system based on map expressions

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Embodiment Construction

[0047] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the associated drawings. Typical embodiments of the invention are shown in the drawings. However, the present invention can be embodied in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of the present invention will be thorough and complete. In addition, it should be noted that when a detailed description of known functions and designs would dilute the protection scope of the present invention, the description may be briefly explained or ignored.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field of the invention. The terms used herein in the description of the present invention are for the purpose of describing spec...

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Abstract

The invention discloses an emotion detection method and system based on map expressions. The method includes collecting text data with map expressions, preprocessing the collected data, and convertinginto a word vector data set; and inputting a data set generated by a data preprocessing unit into a trained network model, the network model outputting a corresponding emotion detection result. The emotion detection method based on map expressions can realize emotion detection of user comments and the like; further, a multiple neural network and an attention mechanism are combined, thereby improving the identification degree of map expressions related with various emotions; the model is trained through an iterator, thereby saving internal storage; and through transfer learning, parameters that can be trained are gradually unfrozen, the risk of over-fitting after transfer learning is reduced as much as possible, and the characteristics of a pre-training model are fully kept.

Description

technical field [0001] The invention relates to the field of emotion analysis, in particular to an emotion detection method and system based on emoticon stickers. Background technique [0002] With the continuous development of the network and the rapid growth of social platforms, the Internet is flooded with a large amount of unstructured text data, which contains a lot of valuable information that cannot be easily processed and extracted. Therefore, if it is possible to automatically extract emotions from data such as Weibo and comments, it can be applied to positive and negative word-of-mouth monitoring of products or services, thereby improving product functions or service processes. Contents of the invention [0003] The technical problem to be solved by the present invention is to provide an emotion detection method and system based on emoticon stickers for the above-mentioned defects of the prior art. [0004] The technical solution adopted by the present invention...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/08G06F17/22
CPCG06N3/08G06F40/151G06F18/241
Inventor 陈雷张桐
Owner SHENZHEN NAIFEI TECH CO LTD
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