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A Method for Automatically Generating Sentiment Curves of Fiction Texts and Predicting Recommendations

An automatic text generation technology, applied in natural language data processing, instruments, computing and other directions, can solve the problem of lack of consideration of the overall emotional change characteristics of novel texts

Active Publication Date: 2020-06-02
NANJING UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] Purpose of the invention: The present invention is mainly aimed at the lack of consideration of the overall emotional change characteristics of novel texts in the existing novel text analysis, and proposes a method that can comprehensively examine the similarities and differences of emotional changes between different texts, and through the machine learning process A method for predicting and recommending novel correlation statistics

Method used

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  • A Method for Automatically Generating Sentiment Curves of Fiction Texts and Predicting Recommendations
  • A Method for Automatically Generating Sentiment Curves of Fiction Texts and Predicting Recommendations
  • A Method for Automatically Generating Sentiment Curves of Fiction Texts and Predicting Recommendations

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Experimental program
Comparison scheme
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Embodiment 1

[0106] Present embodiment is as follows in generating the emotion curve experiment of novel text:

[0107] 11. Input the training text corpus and test text corpus, and get the text word list after preprocessing.

[0108] 12. Use the word list obtained in step 11 to generate the emotional curve of the text, and generate a comparative emotional curve as a comparison according to the previous method.

Embodiment 2

[0110] In this embodiment, the download prediction experiment is given as follows by comparing the emotional curves of novel texts:

[0111] 11. Input the training text corpus and test text corpus, and get the text word list after preprocessing.

[0112] 12. Use the word list obtained in step 11 to generate the sentiment curve of the text.

[0113] 13. Calculate the dynamic time warped distance matrix of the emotional curve.

[0114] 14. The logarithmic download of the test text is given by the distance matrix and the modified Gaussian process.

Embodiment 3

[0116] In this embodiment, the relevant text recommendation experiment is given as follows by comparing the emotional curves of novel texts:

[0117] 11. Input the training text corpus and test text corpus, and get the text word list after preprocessing.

[0118] 12. Use the word list obtained in step 11 to generate the sentiment curve of the text.

[0119] 13. Calculate the dynamic time warped distance matrix of the emotional curve.

[0120] 14. Sorting related texts through the dynamic time warping distance matrix and recommending according to the distance from small to large.

[0121] The purpose of the present invention is to improve the emotion curve generation method of novel texts and make relevant prediction recommendations. It needs to be able to more accurately reflect the emotional change characteristics of the original text, and improve the positive correlation of predicted downloads. In order to verify the validity of the present invention, the present invention...

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Abstract

The invention discloses a method for automatically generating a novel text emotion curve and carrying out prediction recommendation. The emotion curve generated by the invention can more accurately reflect a text emotion change situation. A relationship between the novel text emotion curves can be innovatively utilized to predict novel statistics, and provided downloading amount prediction exhibits higher positive relevance. The recommendation of a related novel text provides a brand new angle for a relevant recommendation task. The method comprises the following main steps that: after a novel text corpus is preprocessed, obtaining the word list of the novel, calculating emotional scores in sequence through a text window, and carrying out converging to obtain the emotion curve; through the emotion curve, calculating a dynamic time regular distance matrix between every two texts; for the dynamic time regular distance matrix, using an improved Gaussian process to give the downloading amount prediction; and according to the dynamic time regular distance matrix, giving relevant text recommendation.

Description

technical field [0001] The invention belongs to the field of sentiment analysis in computer natural language processing, and relates to a method for automatically generating novel text sentiment curves and predicting and recommending them. Background technique [0002] Psychological research has shown that people tend to like stories with familiar patterns and dislike those that contradict their own experience. Kurt Vonnegut believes that the emotional curve of a story is the core embodiment of a novel's reading value; good novels often have similar emotional change patterns. In order to better analyze the emotional changes of novel texts, it is necessary to generate emotional curves of novel texts and conduct related comparative analysis. [0003] The problem about the generation of emotional curves in novel texts is still in the stage of exploration. Although there are various sentiment analysis and evaluation methods for paragraphs and short texts, for the task of gener...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/335G06F40/289
CPCG06F16/335G06F40/289
Inventor 戴新宇周启元黄书剑陈家骏张建兵
Owner NANJING UNIV
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