Sentiment analysis of Japanese text and vocabulary learning based on natural language processing and SVM
| Autor: | Gang Song |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Computer science business.industry Sentiment analysis Computational intelligence 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Weighting Support vector machine Word lists by frequency 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing Artificial intelligence tf–idf business computer Natural language Natural language processing 0105 earth and related environmental sciences |
| Zdroj: | Journal of Ambient Intelligence and Humanized Computing. |
| ISSN: | 1868-5145 1868-5137 |
| Popis: | The traditional Japanese text sentiment classification mainly relies on some domain experts to classify text sentiment according to their knowledge, which is time-consuming and laborious. In order to improve the effect of text sentiment classification, this study combines intelligent machine algorithms to study Japanese text sentiment classification, which provides a theoretical reference for subsequent natural language research. Moreover, this study combines the TF-IDF algorithm with SVM to construct a Japanese text sentiment classification model and proposes a chi-square statistic that combines word frequency factor, inter-class concentration coefficient, and correction coefficient. Aiming at the problem of traditional feature weighting method TFIDF ignoring the distribution of feature items inter-class and intra-class when calculating the weight of feature items, chi-square statistics is introduced to improve the effect that TFIDF ignores the distribution of feature items in inter-class, and intra-class information entropy is introduced to improve the effect that TFIDF ignores the distribution of feature items in the intra-class. In addition, this study designs a control experiment to analyze the performance of the model proposed in this study. Through various comparative analysis, it shows that the research model has good comprehensive performance, meets the needs of sentiment classification system, and can provide theoretical reference for related research. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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