Deep Learning Framework for Identifying Future Market Opportunities from Textual User Reviews
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AGC9UVHSZ" target="_blank" >RIV/00216208:11320/25:GC9UVHSZ - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2024.clib-1.26" target="_blank" >https://aclanthology.org/2024.clib-1.26</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Deep Learning Framework for Identifying Future Market Opportunities from Textual User Reviews
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the Sixth International Conference on Computational Linguistics in Bulgaria (CLIB 2024)
ISBN
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ISSN
2367-5578
e-ISSN
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Number of pages
8
Pages from-to
241-248
Publisher name
Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences
Place of publication
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Event location
Sofia, Bulgaria
Event date
Jan 1, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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