<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Exploring the textual emotional value of the Reader is to help readers understand the Reader’s embedded emotions in an all-around way. In this paper, two text analysis methods, latent semantic analysis and probabilistic latent semantic analysis are described, starting from the analysis model of text research, and the automatic text classification technique is illustrated. The principles of support vector machines are explained, the optimal decision function selection solution is performed using kernel functions, and a PLSA-SVM text analysis model is also constructed using the PLSA method jointly with SVM. The structural features of Reader magazine were analyzed and explained, and the PLSA-SVM text analysis model was used to analyze the data on the narrative features of Reader magazine. In terms of narrative themes, the main narrative themes of Reader magazine were distributed as affection and love, which increased by 16.84% and 17.42% from 2012 to 2021, respectively. In terms of narrative perspectives, the proportion of first-person and third-person narrative perspectives is comparable, increasing by 9.03% and 12.3% from 2012 to 2021, respectively. Thus, in the context of big data, the PLSA-SVM text analysis model can be used to analyze the text narrative features of Reader magazine effectively, and Reader magazine can use the analysis to optimize further the text content and issue texts that are attuned to readers’ emotions.</p>
</abstract>

Exploring the textual emotional value of the Reader is to help readers understand the Reader’s embedded emotions in an all-around way. In this paper, two text analysis methods, latent semantic analysis and probabilistic latent semantic analysis are described, starting from the analysis model of text research, and the automatic text classification technique is illustrated. The principles of support vector machines are explained, the optimal decision function selection solution is performed using kernel functions, and a PLSA-SVM text analysis model is also constructed using the PLSA method jointly with SVM. The structural features of Reader magazine were analyzed and explained, and the PLSA-SVM text analysis model was used to analyze the data on the narrative features of Reader magazine. In terms of narrative themes, the main narrative themes of Reader magazine were distributed as affection and love, which increased by 16.84% and 17.42% from 2012 to 2021, respectively. In terms of narrative perspectives, the proportion of first-person and third-person narrative perspectives is comparable, increasing by 9.03% and 12.3% from 2012 to 2021, respectively. Thus, in the context of big data, the PLSA-SVM text analysis model can be used to analyze the text narrative features of Reader magazine effectively, and Reader magazine can use the analysis to optimize further the text content and issue texts that are attuned to readers’ emotions.

Text Study of Reader Magazine in the Context of Big Data

Shanxi University of Finance and Economics

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Text Study of Reader Magazine in the Context of Big Data"}

Exploring the textual emotional value of the Reader is to help readers understand the Reader’s embedded emotions in an all-around way. In this paper, two text...

Text Study of Reader Magazine in the Context of Big Data

Online veröffentlicht: 04. Sept. 2023

Seitenbereich: -

Eingereicht: 18. Nov. 2022

Akzeptiert: 15. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00284

Schlüsselwörter
Latent semantic analysis, PLSA, Support vector machine, PLSA-SVM model, The Reader

© 2023 Limin Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Text Study of Reader Magazine in the Context of Big Data

Online veröffentlicht: 04. Sept. 2023

Seitenbereich: -

Eingereicht: 18. Nov. 2022

Akzeptiert: 15. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00284

SchlüsselwörterLatent semantic analysis, PLSA, Support vector machine, PLSA-SVM model, The Reader

© 2023 Limin Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Latent semantic analysis, PLSA, Support vector machine, PLSA-SVM model, The Reader