This repository is created for sharing materials (e.g., sample data, trained models, and demo files) for our work. The demo files in the repository allow users to run our models on their own data or on sample data that we provide. The repository includes the following four components:

1. A code demonstration of review text preprocessing. (ReviewPreprocess.zip)

2. The lexicon and a code demonstration of using the lexicon to generate input for the two lexicon-based classificatio (...)

   This repository is created for sharing materials (e.g., sample data, trained models, and demo files) for our work. The demo files in the repository allow users to run our models on their own data or on sample data that we provide. The repository includes the following four components:

   1. A code demonstration of review text preprocessing. (ReviewPreprocess.zip)

   2. The lexicon and a code demonstration of using the lexicon to generate input for the two lexicon-based classification models. (LexiconModels.zip)

   3. The trained Doc2Vec model and a code demonstration of obtaining Doc2Vec embeddings using this model. (Doc2VecEmbeddings.zip)

   4. Trained base-learner classification models (M2, M3, M4), optimized weights for the ensemble model E2, and the trained ensemble model (E3). We also provide a code demonstration of classifying reviews using our proposed models. (ClassificationModels.zip)

   The data used for building these models can be requested from the Global Emancipation Network for approved uses established in a data use agreement.

   This work was funded by the National Science Foundation under award #1936331.

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