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Context: Topic modeling finds human-readable structures in unstructuredtextual data. A widely used topic modeler is Latent Dirichlet allocation. Whenrun on different datasets, LDA suffers from "order effects" i.e. differenttopics are generated if the order of training data is shuffled. Such ordereffects introduce a systematic error for any study. This error can relate tomisleading results;specifically, inaccurate topic descriptions and a reductionin the efficacy of text mining classification results. Objective: To provide amethod in which distributions generated by LDA are more stable and can be usedfor further analysis. Method: We use LDADE, a search-based software engineeringtool that tunes LDA's parameters using DE (Differential Evolution). LDADE isevaluated on data from a programmer information exchange site (Stackoverflow),title and abstract text of thousands ofSoftware Engineering (SE) papers, andsoftware defect reports from NASA. Results were collected across differentimplementations of LDA (Python+Scikit-Learn, Scala+Spark); across differentplatforms (Linux, Macintosh) and for different kinds of LDAs (VEM,or usingGibbs sampling). Results were scored via topic stability and text miningclassification accuracy. Results: In all treatments: (i) standard LDA exhibitsvery large topic instability; (ii) LDADE's tunings dramatically reduce clusterinstability; (iii) LDADE also leads to improved performances for supervised aswell as unsupervised learning. Conclusion: Due to topic instability, usingstandard LDA with its "off-the-shelf" settings should now be depreciated. Also,in future, we should require SE papers that use LDA to test and (if needed)mitigate LDA topic instability. Finally, LDADE is a candidate technology foreffectively and efficiently reducing that instability.

What is Wrong with Topic Modeling? (and How to Fix it Using Search-based Software Engineering)