Case-related news viewpoint sentence sentiment classification method based on CNN-BiLSTM + action model

A technology for sentiment classification and opinion sentences, which is applied in the fields of natural language processing and deep learning, can solve complex feature engineering and manual engineering of rule and statistical machine learning methods, and achieve complex feature engineering and redundant manual work and improve classification effects, effects that reduce effort and complexity

Pending Publication Date: 2020-07-03
KUNMING UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a kind of sentiment classification method based on CNN-BiLSTM+attention model involved in news

Method used

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  • Case-related news viewpoint sentence sentiment classification method based on CNN-BiLSTM + action model
  • Case-related news viewpoint sentence sentiment classification method based on CNN-BiLSTM + action model
  • Case-related news viewpoint sentence sentiment classification method based on CNN-BiLSTM + action model

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Embodiment

[0052] Example: such as Figure 1-Figure 2 As shown, a method of sentiment classification of news opinion sentences involved in the case based on the CNN-BiLSTM+attention model;

[0053] The CNN-BiLSTM+attention model includes: word embedding layer, convolutional layer, pooling layer, BiLSTM layer, Attention attention layer, Softmax classification layer;

[0054] The word embedding layer is used to convert sentence words into low-dimensional word vectors, the convolutional layer is used to automatically extract word features, the pooling layer is used to reduce the dimension of feature vectors, and the BiLSTM layer is Used to memorize long-term dependent serialized information, the Attention layer is used to reinforce important information with a weight matrix, and the Softmax classification layer is used to classify the emotional category with the highest probability;

[0055] The method includes the following steps:

[0056] Step 1: Preprocess the opinion sentences of the news rela...

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Abstract

The invention discloses a case-related news viewpoint sentence sentiment classification method based on a CNN-BiLSTM + action model. The model comprises a word embedding layer, a convolution layer, apooling layer, a BiLSTM layer, an Attention attention layer and a Softmax classification layer. The method comprises the following steps: step 1, preprocessing viewpoint sentences of case-related news, and then encoding all words into word vectors through a word embedding layer; 2, inputting the word vector obtained in the step 1 into a convolution layer, and carrying out convolution operation; 3,the feature vectors obtained after convolution are input into a pooling layer to be subjected to maximum pooling operation; 4, inputting the feature vector obtained after the maximum pooling into a BiLSTM layer; step 5, performing Attention operation on each hidden state vector obtained by the BiLSTM layer; and step 6, classifying the output obtained by the Attention operation through a softmax classification layer to obtain a target emotion category probability. According to the method, complex feature engineering and redundant manual work can be effectively solved, and the workload and complexity can be reduced to a great extent.

Description

Technical field [0001] The present invention relates to the technical fields of natural language processing and deep learning, in particular to a method for categorizing the sentiment of a news opinion sentence involved in a case based on a CNN-BiLSTM+attention model. Background technique [0002] The sentiment classification task of the news opinion sentence involved in the case can be regarded as sentence-level sentiment classification. In the past, the sentiment classification methods were mainly based on dictionary and based on machine learning. Dictionary-based methods generally have an sentiment dictionary. The sentimental words in the dictionary are matched with the words in the sentence, and then rules are designed to analyze the sentiment of the entire sentence; the method based on machine learning usually uses a classification model to perform the sentence Sentiment classification, first extract text features from the data set, and then train a machine learning classifi...

Claims

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Application Information

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IPC IPC(8): G06F16/35G06F40/211G06F40/279G06F40/30G06K9/62G06N3/04G06N3/08
CPCG06F16/35G06N3/08G06N3/044G06N3/045G06F18/241G06F18/2415
Inventor 黄彪李涛
Owner KUNMING UNIV OF SCI & TECH
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