Evaluating the effectiveness of corpus linguistic software in analyzing the grammatical structure: LancsBox and AntConc as case studies

In recent decades, advancements in information technology have proved essential in a variety of disciplines. Linguistics in general, and Critical Discourse Analysis in particular are no exception, as practitioners working within these areas believe that drawing upon certain computerized methods enables them to obtain accurate results from which they can draw valid generalizations. Relying on computer-assisted text analysis has, for this reason, become a necessity especially among critical discourse analysts and has, as a result of that, led to the proliferation of natural language processing programs geared for the analysis of large linguistic corpuses which researchers interested in the study of language can employ. This variety of programs, however, leads one to wonder about their level of accuracy and effectiveness in identifying the specific linguistic features of interest to the researcher, and their ability to reach the same level of preciseness that a manual analysis of the texts under analysis can achieve. The present paper aims, therefore, to compare two of the mostly used types of corpus linguistics programs to analyze linguistic data in the area of textual analysis to find out about the extent to which there are similarities between them, in terms of the findings they yield. It will draw some comparisons between two programs, namely LancsBox and AntConc, in order to find out about their effectiveness in culling the linguistic features such as passive and nominalized constructions from large amounts of linguistic data. We will after that proceed to comparing them to the results obtained through a manual analysis of the data to see if there are any differences. A corpus of ten news articles published in The Times online newspaper were downloaded and analyzed both manually and digitally so as to examine the occurrences and the distribution of the two aforementioned grammatical constructions in the news reports. The findings show that corpus linguistic software can reliably extract passives and active clauses from the texts. Although both LancsBox and AntConc revealed approximately the same frequencies compared to the findings we obtained manually, both programs did not help in specifically isolating the clauses in which the perpetrator of the action that the articles talk about.