@inproceedings{kralev-2024-deep,
    title = "Deep Learning Framework for Identifying Future Market Opportunities from Textual User Reviews",
    author = "Kralev, Jordan",
    booktitle = "Proceedings of the Sixth International Conference on Computational Linguistics in Bulgaria (CLIB 2024)",
    month = sep,
    year = "2024",
    address = "Sofia, Bulgaria",
    publisher = "Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.clib-1.26/",
    pages = "241--248",
    abstract = "The paper develops an application of design gap theory for identification of future market segment growth and capitalization from a set of customer reviews for bought products from the market in a given past period. To build a consumer feature space, an encoded-decoder network with attention is trained over the textual reviews after they are pre-processed through tokenization and embedding layers. The encodings for product reviews are used to train a variational auto encoder network for representation of a product feature space. The sampling capabilities of this network are extended with a function to look for innovative designs with high consumer preferences, characterizing future opportunities in a given market segment. The framework is demonstrated for processing of Amazon reviews in consumer electronics segment."
}