EMOTIONAL BEHAVIOR ANALYSIS OF NOVEL CHARACTERS BASED ON COMPLEX NETWORK AND WLDA ALGORITHM — TAKING HARUKI MURAKAMI'S NORWEGIAN FOREST AS AN EXAMPLE
Bibliographic record
Abstract
Abstract Background In recent years, with the rapid development of public network, emotion analysis has always been a research hotspot in the field of natural language processing and data mining. The current research mainly focuses on various comments on the Internet, and there is relatively little analysis of the psychological activities of the characters and the emotional changes of the text in the novel. How to use computer technology to identify the emotional tendency and psychological activities of characters in literary works has important practical significance. Topics and Methods This paper uses complex network and wlda algorithm to analyze the mood of Norwegian forest. Using complex network is to preprocess the data, extract the information in the article by using word frequency statistics, then build a complex network according to word frequency, find out the key points of complex network according to the principle of structural hole, complex network, and analyze the emotional tendency to be expressed in the article. The wlda algorithm model is used to segment the data, remove the stop words, and then the algorithm is used to verify the emotional tendency of the novel. The corpus used in the experiment is Haruki Murakami's novel Norwegian forest. The emotion seed words used in the experiment are from the Chinese word set used for emotion analysis in the Internet HowNet. The algorithm parameters take the data commonly used in wlda model, where 50 is equal to 0.01, and the number of keywords to judge the subject's emotional tendency is C, which is equal to 100. Readers' emotion algorithm, this study uses the relevant scale to investigate. (1) Positive emotion scale. The Panas emotion scale developed by Wason et al. Is widely used to measure emotion. The scale includes two dimensions: positive emotion and negative emotion. There are 6 questions in this dimension. In addition, the boredom tendency questionnaire was used to investigate internet boredom. The boredom tendency questionnaire was prepared by Huang Shihua et al. In 2010. The research shows that the scale has high reliability and validity. The scale has 30 items and is scored by Likert 7 points (from 7 to 1 means “completely agree” to “completely disagree”, and 4 means neutral). The scale includes two sub questionnaires of external stimulation and internal stimulation. The external stimulus sub questionnaire includes four factors: monotonicity, loneliness, tension and restraint. The internal stimulation sub questionnaire contains two factors: self-control and creativity. The higher the questionnaire score, the higher the boredom tendency. Group learning burnout scale group learning burnout scale was compiled by Lian Rong et al in 2005. The research shows that the scale has high reliability and validity. The scale has 20 items and uses a 5-level scoring method (from 5 to 1 means “completely consistent” to “completely inconsistent”, and 3 means neutral). It includes three dimensions, including depression, improper behavior and low sense of achievement. Emotion regulation strategy scale emotion regulation style scale was compiled by gross et al in 2003. The Chinese version of the scale has been proved to have high reliability and validity. The scale has 10 items and adopts Likert 7-point scoring (from 7 to 1 means “fully agree” to “completely disagree”, and 4 means neutral). The scale includes two sub questionnaires: cognitive reappraisal and expression inhibition. Data Analysis Adopt spss16 0 and amos17 0 statistical software Line statistical processing. Results In the process of simplifying complex network, the frequency of low-frequency words was 1. Experiments show that the results of complex network and wlda algorithm model are basically consistent, and the effect of emotion analysis is obvious. Readers' emotional response and emotional effect are also basically the same. Conclusion Some high-frequency but meaningless stop words in the corpus have caused great interference to the reasoning of the model topic. Therefore, when analyzing the text, we need to preprocess the corpus and filter out low-frequency words, which affects the emotion extraction to a certain extent. In the process of simplifying complex networks, it is also necessary to adjust the threshold of filtered low-frequency words according to different work. The experimental results show that the negative tendency is greater than the positive tendency, and the whole text expresses the negative emotion, that is, the sadness and confusion of survival. Acknowledgements Supported by the doctoral startup Research (No.20rc15), the research on the influence of consumers' purchase intention of traceable agricultural products (No.20rc03), and the design of precision control system based on the Internet of things (No.202103001).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".