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  1. Mitrovic, A. ; Bosch, N. (Ed.)
    Regular expression (regex) coding has advantages for text analysis. Humans are often able to quickly construct intelligible coding rules with high precision. That is, researchers can identify words and word patterns that correctly classify examples of a particular concept. And, it is often easy to identify false positives and improve the regex classifier so that the positive items are accurately captured. However, ensuring that a regex list is complete is a bigger challenge, because the concepts to be identified in data are often sparsely distributed, which makes it difficult to identify examples of \textit{false negatives}. For this reason, regex-based classifiers suffer by having low recall. That is, it often misses items that should be classified as positive. In this paper, we provide a neural network solution to this problem by identifying a \textit{negative reversion set}, in which false negative items occur much more frequently than in the data set as a whole. Thus, the regex classifier can be more quickly improved by adding missing regexes based on the false negatives found from the negative reversion set. This study used an existing data set collected from a simulation-based learning environment for which researchers had previously defined six codes and developed classifiers with validated regex lists. We randomly constructed incomplete (partial) regex lists and used neural network models to identify negative reversion sets in which the frequency of false negatives increased from a range of 3\\%-8\\% in the full data set to a range of 12\\%-52\\% in the negative reversion set. Based on this finding, we propose an interactive coding mechanism in which human-developed regex classifiers provide input for training machine learning algorithms and machine learning algorithms ``smartly" select highly suspected false negative items for human to more quickly develop regex classifiers. 
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  2. Barany, A. ; Damsa, C. (Ed.)
    Regular expression (regex) based automated qualitative coding helps reduce researchers’ effort in manually coding text data, without sacrificing transparency of the coding process. However, researchers using regex based approaches struggle with low recall or high false negative rate during classifier development. Advanced natural language processing techniques, such as topic modeling, latent semantic analysis and neural network classification models help solve this problem in various ways. The latest advance in this direction is the discovery of the so called “negative reversion set (NRS)”, in which false negative items appear more frequently than in the negative set. This helps regex classifier developers more quickly identify missing items and thus improve classification recall. This paper simulates the use of NRS in real coding scenarios and compares the required manual coding items between NRS sampling and random sampling in the process of classifier refinement. The result using one data set with 50,818 items and six associated qualitative codes shows that, on average, using NRS sampling, the required manual coding size could be reduced by 50% to 63%, comparing with random sampling. 
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  3. Weinberger, A. ; Chen, W. ; Hernández-Leo, D. ; Chen, B. (Ed.)
    In this paper, we describe iPlan, a web-based software platform for constructing localized, reduced-form models of land-use impacts, enabling students, civic representatives, and others without specialized knowledge of land-use planning practices to explore and evaluate possible solutions to complex, multi-objective land-use problems in their own local contexts. 
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