Domain-Adaptive Sentiment Analysis Across Online Social Networks

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

Domain-Adaptive Sentiment Analysis Across Online Social Networks

Popis výsledku v původním jazyce

"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."

Název v anglickém jazyce

Domain-Adaptive Sentiment Analysis Across Online Social Networks

Popis výsledku anglicky

"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."

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

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Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů