Thanks to a post by Jason Anderson I read a paper called – Research trends in applied linguistics from 2005 to 2016: A bibliometric analysis and its implications.
The authors kindly sent me a file of the abstracts that they had collected. I thought some topic modelling would be interesting to do on the data. Topic modelling is a way to discern what a set of documents is “about” by getting a program to find clusters of words. Lei & Liu (2018) used a different approach called n-grams to find their topics.
They found for example that between 2005-2016 there was a significant decrease in formal linguistic issues, such as phonology and syntax.
The topic modelling also shows this decrease (note that the corpus used in the topic modelling runs from 2000-2016):
By contrast they found significant increases in topics related to sociocultural issues. The topic modelling also indicates this:
The topic model correlational matrix is interesting to look at. The screenshot below shows that the topic “english chinese paper” (full topic cluster is “english chinese paper hong use varieties kong world local language”) is significantly related to the topic “language social identity” (full cluster is “language social identity how practices literacy languages policy linguistic multilingual”):
Though I am not sure how to interpret the red blobs! If you do let me know.
Finally the model indicates that topics related to child language development seem to be on the wane (full cluster is “children language age children’s development early study acquisition adults years”):
Have a play with the model and if you spot anything interesting do leave a comment. Note running iterations can be a tad slow.
Thanks for reading.
Lei, L., & Liu, D. (2018). Research Trends in Applied Linguistics from 2005 to 2016: A Bibliometric Analysis and Its Implications. Applied Linguistics.