The Effectiveness of CATA Software in Exploring the Significance of Modal Verbs in Large Data Texts

This paper investigates the effectiveness of using and applying CATA (Computer-Aided Text Analysis) software in exploring the extent to which particular modals are significant in communicating the ideological and thematic messages of literary discourse. More specifically, the paper attempts to test the hypothesis that CATA software, including FDA (Frequency Distribution Analysis), KWICK (Key Word in Context), CA (Content Analysis), and TDA (Thematic Distribution Analysis) are effectively helpful in the linguistic and ideological analysis of modals in literary texts. To this end, the paper uses the frequency distribution analysis (FDA) and applies it to Edward Bond’s Lear as a sample representing literary texts. Two modal verbs were selected to be computationally analyzed by means of the frequency distribution analysis in order to decode the different ideologies they carry in the discourse of the selected play. These are will and must. These modal verbs were computationally displayed within their contextual, total and indicative occurrences in the play under investigation to demonstrate the way they convey particular ideologies. Findings revealed that CATA software represented in its variable of FDA is highly contributive to communicating ideologies in the play under investigation. The paper further demonstrated two findings: first, via CATA software, analysts can easily arrive at the ideological significance of the various classes of words, including modal verbs that are used in literary texts; and, second, the analysis showed that only a few occurrences out of the total number of frequencies of the modal verbs at hand are indicative in conveying the hidden ideologies of their users.

@article{Khafaga2022,
  title     = {The Effectiveness of CATA Software in Exploring the Significance of Modal Verbs in Large Data Texts},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {2},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Ayman Farid Khafaga},
  doi       = {10.14569/IJACSA.2022.0130292},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130292}
}