Towards an Author-Topic-Term-Model Visualization of 100 Years of German Sociological Society Proceedings
This work addresses the need for more comprehensive science mapping in sociology, but it is incremental as it builds on existing statistical text analysis techniques.
The authors tackled the problem of enhancing scientometric analysis by integrating term frequencies from text bodies with Bayesian methods, and they introduced a new dataset and model to explore this approach, presenting initial results.
Author co-citation studies employ factor analysis to reduce high-dimensional co-citation matrices to low-dimensional and possibly interpretable factors, but these studies do not use any information from the text bodies of publications. We hypothesise that term frequencies may yield useful information for scientometric analysis. In our work we ask if word features in combination with Bayesian analysis allow well-founded science mapping studies. This work goes back to the roots of Mosteller and Wallace's (1964) statistical text analysis using word frequency features and a Bayesian inference approach, tough with different goals. To answer our research question we (i) introduce a new data set on which the experiments are carried out, (ii) describe the Bayesian model employed for inference and (iii) present first results of the analysis.