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        <title>Deep Learning Framework for Identifying Future Market Opportunities from Textual User Reviews</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Deep Learning Framework for Identifying Future Market Opportunities from Textual User Reviews
%A Kralev, Jordan
%S Proceedings of the Sixth International Conference on Computational Linguistics in Bulgaria (CLIB 2024)
%D 2024
%8 September
%I Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences
%C Sofia, Bulgaria
%F kralev-2024-deep
%X 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.
%U https://aclanthology.org/2024.clib-1.26
%P 241-248

Markdown (Informal)

[Deep Learning Framework for Identifying Future Market Opportunities from Textual User Reviews](https://aclanthology.org/2024.clib-1.26) (Kralev, CLIB 2024)

• Deep Learning Framework for Identifying Future Market Opportunities from Textual User Reviews (Kralev, CLIB 2024)

ACL

• Jordan Kralev. 2024. Deep Learning Framework for Identifying Future Market Opportunities from Textual User Reviews. In Proceedings of the Sixth International Conference on Computational Linguistics in Bulgaria (CLIB 2024), pages 241–248, Sofia, Bulgaria. Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences.