Emotion instability user detection method for video conventional comments

A technology that stabilizes users and detection methods, applied in the field of review text classification, and can solve problems such as unresolved reviewers, changes, etc.

Active Publication Date: 2021-01-12
XIHUA UNIV
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  • Description
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  • Application Information

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Problems solved by technology

Nor does it address the fact that the sentiment of reviewers tends to change over time

Method used

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  • Emotion instability user detection method for video conventional comments
  • Emotion instability user detection method for video conventional comments
  • Emotion instability user detection method for video conventional comments

Examples

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Embodiment

[0105] (1) Data collection. A web crawler is used to obtain 349,612 regular comments of time-synchronized comments on the bilibili website. The data of the comment dataset comes from videos of 13 chapters of a series of works, which are stored from EP1-EP13 in the order of video release, and the interval between video updates and releases is 7 days , the total number of days that comment data persists is 545. Since there is currently no public and reliable bilibili website time-synchronized comment and regular comment sentiment classification dataset, in order to train and verify the sentiment analysis method, 9000 regular comments were randomly crawled as a sentiment classification dataset and marked.

[0106] (2) When users post regular comments, they can use the preset emoticons on the website, and add emoticons to the comment text to form a richer expression. Collect and organize the emoticon packages of the website, build the corresponding emoticon and text comparison ta...

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Abstract

The invention discloses an emotion instability user detection method for video conventional comments, which comprises the following steps of: 1, collecting original data from a website, and screeningout a conventional comment text, user information and the comment time; 2, achieving formatting processing of the data to form a conventional comment set; 3, summarizing preset expression packages ofthe website, constructing an expression comparison table, and measuring emotional tendency of conventional comments to achieve emotional analysis of the conventional comments. 4, constructing a time sequence comment interaction network; and step 5, judging whether the user conforms to the definition of the user with unstable emotion or not according to the time sequence inter-user relationship, soas to detect the user with unstable emotion. The method has the advantages that emotion-variable users are found according to conventional comment content in the video in the early stage of public opinion development, great convenience is brought to control and guidance of public opinions, and good social benefits and economic values are generated.

Description

technical field [0001] The invention relates to the technical field of comment text classification, in particular to a method for detecting emotionally unstable users oriented to regular video comments. Background technique [0002] Online video is one of people's leisure and entertainment methods. After watching the video, users often post comments in two ways: (1) user barrage. (2) Post regular comments in the comment area of ​​the web page. Use this to describe your overall viewing experience. If the user is affected accordingly in the emotional difference time zone, based on the theory of cognitive dissonance, the user's emotional changes will also be reflected in the regular comment area. In the comment area, users can not only express their own understanding of the video content, but also interact with other users to form comment interactions between users. As time goes by, the number of comment interactions gradually increases, and multiple user comment interaction...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/951G06F16/9535G06N3/04G06N3/08
CPCG06F16/951G06F16/9535G06N3/049G06N3/08G06N3/045
Inventor 杜亚军李若淼赵飞宇
Owner XIHUA UNIV
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