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English research articles (RAs) are an essential genre in academia, so theattempts to employ NLP to assist the development of academic writing abilityhave received considerable attention in the last two decades. However, therehas been no study employing feature engineering techniques to investigate thelinguistic features of RAs of different academic impacts (i.e., the papers ofhigh/moderate citation times published in the journals of high/moderate impactfactors). This study attempts to extract micro-level linguistic features inhigh- and moderate-impact journal RAs, using feature engineering methods. Weextracted 25 highly relevant features from the Corpus of English JournalArticles through feature selection methods. All papers in the corpus deal withCOVID-19 medical empirical studies. The selected features were then validatedof the classification performance in terms of consistency and accuracy throughsupervised machine learning methods. Results showed that 24 linguistic featuressuch as the overlapping of content words between adjacent sentences, the use ofthird-person pronouns, auxiliary verbs, tense, emotional words provideconsistent and accurate predictions for journal articles with differentacademic impacts. Lastly, the random forest model is shown to be the best modelto fit the relationship between these 24 features and journal articles withhigh and moderate impacts. These findings can be used to inform academicwriting courses and lay the foundation for developing automatic evaluationsystems for L2 graduate students.

Automatic Analysis of Linguistic Features in Journal Articles of Different Academic Impacts with Feature Engineering Techniques