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Irony detection method based on intra-sentence word pair relation and context user characteristics

A technology of user characteristics and detection methods, applied in the field of emotion classification, which can solve problems such as inability to capture long-distance dependencies and incoherent sentences

Active Publication Date: 2019-08-23
HANGZHOU DIANZI UNIV
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Problems solved by technology

It not only solves the problem that LSTM sequence modeling cannot capture long-distance dependencies, but also highlights the incongruity within the sentence

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  • Irony detection method based on intra-sentence word pair relation and context user characteristics
  • Irony detection method based on intra-sentence word pair relation and context user characteristics
  • Irony detection method based on intra-sentence word pair relation and context user characteristics

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Embodiment Construction

[0021] The present invention will be further described below in conjunction with the accompanying drawings.

[0022] refer to figure 1 , figure 2 , image 3 , Figure 4 and Figure 5 , an irony detection method based on intra-sentence word-pair relations and contextual user characteristics, including the following steps:

[0023] Step 1. Preprocess the comment text that needs irony detection:

[0024] 1.1. Delete words that appear only once in the entire corpus and replace them with UNK tags;

[0025] 1.2. Delete comments with less than 5 words;

[0026] 1.3. Use the W2V word vector model to represent each comment as a column of word vectors no i is the sentence length.

[0027] Step 2. Use the self-attention mechanism to model the association of each word to obtain the intra-sentence attention representation:

[0028] 2.1 pair input sequence Each word in models the relationship (between words): in, is a parameter that needs to be learned through training. ...

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Abstract

The invention discloses an irony detection method based on an intra-sentence word pair relation and context user characteristics. A self-attention mechanism (also known as an internal attention mechanism) is used for analyzing word pairs with emotional polarity contradiction in a text. The method comprises the following steps: learning and fusing writing style characteristics and character characteristics of a user to obtain user embeddings as irony detection context information; and meanwhile, the sequence information of the text is coded in combination with an LSTM network. The method can better detect the irony expression, and can obtain good accuracy under the condition that the irony expression is obvious or hidden. Specifically, the invention discloses an irony detection model basedon an intra-sentence word pair relation and context user characteristics. The method provides a judgment basis for irony detection, and is beneficial to judgment of the anti-text without obvious contradiction word pairs. Therefore, the irony detection accuracy is improved from the two aspects.

Description

technical field [0001] The invention relates to the field of emotion classification, in particular to an irony detection method based on the relationship between word pairs in a sentence and the characteristics of contextual users. Background technique [0002] Irony and sarcasm are rhetorical methods commonly used in social media. Irony is the use of words contrary to the original meaning to express the meaning, but with negative, ironic and mocking meanings. Sarcasm is the use of metaphors, exaggerations, etc. to expose, criticize, or ridicule people or things. Regarding the relationship between irony and sarcasm, it can be considered that sarcasm is a kind of irony that contains emotions (such as aggressive emotions). In this paper, irony and satire are collectively referred to as "irony", and no distinction is made between irony and satire. The figurative nature of irony poses a great challenge to the sentiment analysis task. [0003] At present, the research on iron...

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Application Information

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IPC IPC(8): G06F16/35G06K9/62
CPCG06F16/35G06F18/253
Inventor 姜明张雯张旻汤景凡戚铖杰腾海滨
Owner HANGZHOU DIANZI UNIV
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