Domain-Adaptive Sentiment Analysis Across Online Social Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AZQE33GJT" target="_blank" >RIV/00216208:11320/23:ZQE33GJT - isvavai.cz</a>
Result on the web
<a href="https://openrepository.aut.ac.nz/items/463c433b-a969-4f30-9872-b152ba1bba1a" target="_blank" >https://openrepository.aut.ac.nz/items/463c433b-a969-4f30-9872-b152ba1bba1a</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Domain-Adaptive Sentiment Analysis Across Online Social Networks
Original language description
"Aspect-based sentiment analysis is an important task in natural language processing and has a wide range of applications in fields such as e-commerce, marketing, and customer service. The goal of this task is to identify aspect and opinion terms and classify the sentiment expressed towards a particular aspect in a given text. Despite its significance, aspect-based sentiment analysis remains a challenging task due to limitations in existing models. These limitations include an inadequate consideration of crucial implicit linguistic features for aspect term extraction, declining performance on unstructured and small datasets for aspect and relation extraction, a complex and varied model landscape for different sub-tasks, and the time-consuming construction of prompts for cross-domain aspect term extraction. In this thesis, these challenges are tackled by employing several innovative deep neural network models."
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
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
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů