English Text Analysis System Based on Genetic Algorithm

Abstract

In order to accurately extract the useful information in English, this paper studies English text analysis combined with a genetic algorithm and establishes a text analysis system. In this method, a text tendency analysis algorithm based on a genetic algorithm language model is proposed, and a Doc2vec text feature representation algorithm integrating the LDA model is designed; the parallelization technology of text analysis algorithm is studied, and the parallelization model of the algorithm by using spark big data platform is designed; the process of English text tendency analysis is studied, and a Chinese text analysis system is designed and implemented based on big data platform, including corpus intake, corpus annotation, corpus storage, model training, model verification, and other modules. In order to verify the feasibility of this subject, the accuracy of the Doc2vec text feature representation algorithm of the fused LDA model designed in the prototype system is tested. The experimental results show that the fused text representation model has high recognition degree, and the AUC value of the ROC curve reaches 0.95. At the same time, this paper tests the text analysis-related algorithms involved in the system. The test results show that the parallel algorithm can greatly improve the efficiency of the system.

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“…Content analysis is widely employed in the social science field due to its ability to provide an objective, systematic, and quantitative description of specific forms of communication. It allows for an investigation into the focus, internal tendencies, and changing patterns of communication content over time, providing insights into social reality from a content-based perspective [50]. Given that the selected data in this study consists of textual data published by the government, content analysis is a feasible approach for analysis.…”

“…Content analysis encompasses several steps, including text input, word segmentation, removal of symbols and nonsensical stop words, stemming, and the construction of a document word frequency matrix using a specific encoding method. Through this process, textual data is compressed into phrase frequencies, transforming qualitative text data into quantitative frequencies [50]. These indices can provide answers to more quantitatively oriented research questions.…”