  English research articles (RAs) are an essential genre in academia, so the
attempts to employ NLP to assist the development of academic writing ability
have received considerable attention in the last two decades. However, there
has been no study employing feature engineering techniques to investigate the
linguistic features of RAs of different academic impacts (i.e., the papers of
high/moderate citation times published in the journals of high/moderate impact
factors). This study attempts to extract micro-level linguistic features in
high- and moderate-impact journal RAs, using feature engineering methods. We
extracted 25 highly relevant features from the Corpus of English Journal
Articles through feature selection methods. All papers in the corpus deal with
COVID-19 medical empirical studies. The selected features were then validated
of the classification performance in terms of consistency and accuracy through
supervised machine learning methods. Results showed that 24 linguistic features
such as the overlapping of content words between adjacent sentences, the use of
third-person pronouns, auxiliary verbs, tense, emotional words provide
consistent and accurate predictions for journal articles with different
academic impacts. Lastly, the random forest model is shown to be the best model
to fit the relationship between these 24 features and journal articles with
high and moderate impacts. These findings can be used to inform academic
writing courses and lay the foundation for developing automatic evaluation
systems for L2 graduate students.
