attention CNNs and CCR-based text sentiment analysis method

A sentiment analysis and text technology, applied in semantic analysis, neural learning methods, special data processing applications, etc., can solve problems such as single feature, lack of global features, and insufficient to reflect the emotional polarity of text

Active Publication Date: 2017-08-25
CHONGQING UNIV OF POSTS & TELECOMM
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AI Technical Summary

Problems solved by technology

[0004] The present invention proposes a text sentiment analysis method based on attention CNNs combined with convolutional neural network and CCR multi-mode consistent regression. By analyzing the emotional polarity of the word seg...

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  • attention CNNs and CCR-based text sentiment analysis method
  • attention CNNs and CCR-based text sentiment analysis method
  • attention CNNs and CCR-based text sentiment analysis method

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Embodiment Construction

[0042] The method of the present invention comprises the following steps: 1. Utilize original text data to train semantic word vectors and emotional word vectors and utilize the sentiment dictionary collected to carry out dictionary word vector construction; 2. Utilize the context semantics of long short-term memory network LSTM to capture words for ambiguity Elimination; 3. Using convolutional neural network (combining convolution kernels with different filter lengths to extract local features of the text; 4. Using three different attention mechanisms to extract global features respectively; 5. Extracting artificial features from the original text data 6. Utilize local features, global features and artificial features to train the multi-mode consistent regression objective function; 7. Utilize the multi-mode consistent regression prediction method to carry out emotional polarity prediction. The present invention is relative to adopting single word vector or only extracting text...

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Abstract

The invention discloses an attention CNNs and CCR-based text sentiment analysis method and belongs to the field of natural language processing. The method comprises the following steps of 1, training a semantic word vector and a sentiment word vector by utilizing original text data and performing dictionary word vector establishment by utilizing a collected sentiment dictionary; 2, capturing context semantics of words by utilizing a long-short-term memory (LSTM) network to eliminate ambiguity; 3, extracting local features of a text in combination with convolution kernels with different filtering lengths by utilizing a convolutional neural network; 4, extracting global features by utilizing three different attention mechanisms; 5, performing artificial feature extraction on the original text data; 6, training a multimodal uniform regression target function by utilizing the local features, the global features and artificial features; and 7, performing sentiment polarity prediction by utilizing a multimodal uniform regression prediction method. Compared with a method adopting a single word vector, a method only extracting the local features of the text, or the like, the text sentiment analysis method can further improve the sentiment classification precision.

Description

technical field [0001] The invention is a method for analyzing text emotion, which belongs to the field of natural language processing. Background technique [0002] With the rise of social platforms such as Twitter, Facebook, and Weibo, and e-commerce platforms such as Amazon and Taobao, commentary text resources on the Internet are increasing day by day. Faced with a large number of unstructured comment texts from Weibo and forums, it is urgent to analyze and judge the emotional tendency expressed in the text through natural language processing technology. For example, identifying the emotional information of product attributes from comments can provide decision support for merchants and other users; in public opinion monitoring, the government can keep abreast of people's attitudes towards emergencies and social phenomena, and guide public opinion trends. The vast majority of traditional sentiment analysis uses the combination of traditional NLP features and machine lear...

Claims

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Application Information

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IPC IPC(8): G06F17/27G06N3/04G06N3/08
CPCG06N3/08G06F40/289G06F40/30G06N3/045
Inventor 张祖凡邹阳甘臣权
Owner CHONGQING UNIV OF POSTS & TELECOMM
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