EMOTIONAL BEHAVIOR ANALYSIS OF NOVEL CHARACTERS BASED ON COMPLEX NETWORK AND WLDA ALGORITHM — TAKING HARUKI MURAKAMI'S NORWEGIAN FOREST AS AN EXAMPLE
Notice bibliographique
Résumé
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).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».